The product will help consumer product founders incubate and scale omni-channel e-commerce brands

Predicate

predicate

Mycroft — A Predicate Logic Language — Overview / Post-mortem

COPA Presents Evidence of Bitcoin White Paper Fabrication by Craig Wright 

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 19 of 42. 17. “LPA.tex” and “LP1.tex” {ID_004722} {PTR-F/79/1} and {ID_004723} {PTRF/80/1} 328. These are two LaTeX source documents presented as if they are precursor work to the Bitcoin White Paper. The first presents as an article with Satoshi named as author, while the second presents as a version of the same article with Dr Wright named as author. Each addresses quorum systems, a subject which Dr Wright now maintains influenced his development of Bitcoin. By their presence in BDOPC.raw, the documents purport to pre-date 31 October 2007. (a) COPA’s Reasons for Alleging Forgery 329. These documents are among the 71 New Reliance Documents that were inserted into the BDO Drive by the editing process and which the parties’ experts agree were manipulated [Madden / Lynch1 [12] Q/6/5]. 330. The section on BDOPC.raw above is repeated. Comparing the deleted versions of these documents to the disclosed versions shows that they did not exist in this form on 17 September 2023. They were modified within BDOPC.raw at some point between 17 September and 19 September 2023. This was done with the computer clock set back to 2007, in order to backdate the document. [PM46 [111 – 117] H/278/38] 331. The modifications made included: 331.1. In respect of {ID_004722}, altering the dates of articles referred to, respectively from “2009” to “2006”, and from “2016” to “?” [PM46 [112] H/278/39]. 331.2. Removing lines of the document relating to the previously-cited papers from 2009 and 2016 (while leaving blank lines in their place) [PM46 [113] H/278/39]. 331.3. In respect of {ID_004723}, deleting lines referring to previously-cited articles dating from after 2007 (while leaving blank lines in their place) [PM46 [117-118] H/278/41]. 331.4. Altering the metadata of the documents such that they appeared to be created earlier than they were [PM46 [111 and 119] H/278/38]. 332. The documents were sourced from BDOPC.raw. The section “BDOPC.raw” above is repeated. These documents were added by the Manipulation User. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 333. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto, contrary to fact. 334. The further effect of tampering is to lend support to Dr Wright’s new position that the Bitcoin White Paper was created in LaTeX. That story is a recent product of Dr Wright’s change in his account. 335. These documents were added to BDOPC.raw by the Manipulation User. The Manipulation User is Dr Wright. the section “BDOPC.raw” above is repeated. 336. Dr Wright has attached particular importance to these documents: 336.1. {ID_004722} is said to be one of Dr Wright’s “LaTeX files which, when compiled, generate draft articles under the pseudonym “Satoshi” or “Satoshi Nakamoto” relating to concepts later used in the Bitcoin White Paper.” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/3] 336.2. {ID_004722} is said to be important to Dr Wright’s case because it is “a LaTeX file coding for an article titled “Predicates in Quorum Systems”, which discusses concepts that underpin the technology of Bitcoin, such as the use of quorum systems to provide security in distributed systems. The author of the paper is stated to be “Satoshi” and the article bears the date 30 August 2006.” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/3] 336.3. {ID_004723} is said to be “a version of the “Predicates in Quorum Systems” paper at {ID_004722}, but listing Dr Wright as the author rather than “Satoshi”. ” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/6] 337. The documents were not disclosed at the proper time. They were disclosed instead from the BDOPC.raw image. BDOPC.raw is not a reliable source because it has been manipulated by Dr Wright. The section “BDOPC.raw” above is repeated. (c) Dr Wright’s Explanations and COPA’s Rebuttal 338. Dr Wright claimed, as set out above, that these were planted documents by Mr AgerHanssen or someone associated with him. {Day5/86:14} He also rejected the use of ChatGPT. 339. COPA submitted that this explanation should be rejected as dishonest for the following reasons: 339.1. If the BDOPC.raw is accepted as being forged, it follows that documents on it should be treated as being forged unless they are documents which Mr Madden says are original to the image that was taken in October 2007. 339.2. The story about these being planted by someone is addressed in earlier examples in this Schedule. 339.3. The differences between the InfoDef09.raw and the versions subsequently disclosed and relied upon showed that the changes were to change tenses from the deleted version (which talked about Bitcoin in the past tense) to the future tense in the disclosed versions. This is consistent with the documents being edited to support his case. 339.4. The deleted earlier versions also contained references to papers post-dating the purported 2007 date, such as ‘Xia et Al’ (2016). All such references were deleted and in the disclosed version replaced by a ‘?’ – clearly to indicate that a reference needed to be added. The evident reason that these, and only these, references were removed, is to remove what would otherwise have been obvious indications that the document post-dated 2007. 339.5. It is clear that Dr Wright did use ChatGPT and these documents included classic indicia of how ChatGPT formats references (set out above). 339.6. Mr Lynch agreed with Mr Madden that ID_0004722 and ID_004723 were manipulated {Q/6/5}. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 19 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 19 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 17. “LPA.tex” and “LP1.tex” {ID_004722} {PTR-F/79/1} and {ID_004723} {PTRF/80/1} 328. These are two LaTeX source documents presented as if they are precursor work to the Bitcoin White Paper. The first presents as an article with Satoshi named as author, while the second presents as a version of the same article with Dr Wright named as author. Each addresses quorum systems, a subject which Dr Wright now maintains influenced his development of Bitcoin. By their presence in BDOPC.raw, the documents purport to pre-date 31 October 2007. (a) COPA’s Reasons for Alleging Forgery (a) COPA’s Reasons for Alleging Forgery 329. These documents are among the 71 New Reliance Documents that were inserted into the BDO Drive by the editing process and which the parties’ experts agree were manipulated [Madden / Lynch1 [12] Q/6/5]. 330. The section on BDOPC.raw above is repeated. Comparing the deleted versions of these documents to the disclosed versions shows that they did not exist in this form on 17 September 2023. They were modified within BDOPC.raw at some point between 17 September and 19 September 2023. This was done with the computer clock set back to 2007, in order to backdate the document. [PM46 [111 – 117] H/278/38] 331. The modifications made included: 331.1. In respect of {ID_004722}, altering the dates of articles referred to, respectively from “2009” to “2006”, and from “2016” to “?” [PM46 [112] H/278/39]. 331.2. Removing lines of the document relating to the previously-cited papers from 2009 and 2016 (while leaving blank lines in their place) [PM46 [113] H/278/39]. 331.3. In respect of {ID_004723}, deleting lines referring to previously-cited articles dating from after 2007 (while leaving blank lines in their place) [PM46 [117-118] H/278/41]. 331.4. Altering the metadata of the documents such that they appeared to be created earlier than they were [PM46 [111 and 119] H/278/38]. 332. The documents were sourced from BDOPC.raw. The section “BDOPC.raw” above is repeated. These documents were added by the Manipulation User. (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility (b) COPA’s Reasons for Inferring Dr Wright’s Knowledge / Responsibility 333. The effect of the tampering is to make the document appear to be supportive of Dr Wright’s claim to be Satoshi Nakamoto, contrary to fact. 334. The further effect of tampering is to lend support to Dr Wright’s new position that the Bitcoin White Paper was created in LaTeX. That story is a recent product of Dr Wright’s change in his account. 335. These documents were added to BDOPC.raw by the Manipulation User. The Manipulation User is Dr Wright. the section “BDOPC.raw” above is repeated. 336. Dr Wright has attached particular importance to these documents: 336.1. {ID_004722} is said to be one of Dr Wright’s “LaTeX files which, when compiled, generate draft articles under the pseudonym “Satoshi” or “Satoshi Nakamoto” relating to concepts later used in the Bitcoin White Paper.” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/3] “LaTeX files which, when compiled, generate draft articles under the pseudonym “Satoshi” or “Satoshi Nakamoto” relating to concepts later used in the Bitcoin White Paper.” 336.2. {ID_004722} is said to be important to Dr Wright’s case because it is “a LaTeX file coding for an article titled “Predicates in Quorum Systems”, which discusses concepts that underpin the technology of Bitcoin, such as the use of quorum systems to provide security in distributed systems. The author of the paper is stated to be “Satoshi” and the article bears the date 30 August 2006.” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/3] “Predicates in Quorum Systems”, which discusses concepts that underpin the technology of Bitcoin, such as the use of quorum systems to provide security in distributed systems. The author of the paper is stated to be “Satoshi” and the article bears the date 30 August 2006.” 336.3. {ID_004723} is said to be “a version of the “Predicates in Quorum Systems” paper at {ID_004722}, but listing Dr Wright as the author rather than “Satoshi”. ” [Wright6 E/21/3; Schedule 1 to Field1, L20/223/6] “Predicates in Quorum Systems” “Satoshi”. 337. The documents were not disclosed at the proper time. They were disclosed instead from the BDOPC.raw image. BDOPC.raw is not a reliable source because it has been manipulated by Dr Wright. The section “BDOPC.raw” above is repeated. (c) Dr Wright’s Explanations and COPA’s Rebuttal (c) Dr Wright’s Explanations and COPA’s Rebuttal 338. Dr Wright claimed, as set out above, that these were planted documents by Mr AgerHanssen or someone associated with him. {Day5/86:14} He also rejected the use of ChatGPT. 339. COPA submitted that this explanation should be rejected as dishonest for the following reasons: 339.1. If the BDOPC.raw is accepted as being forged, it follows that documents on it should be treated as being forged unless they are documents which Mr Madden says are original to the image that was taken in October 2007. 339.2. The story about these being planted by someone is addressed in earlier examples in this Schedule. 339.3. The differences between the InfoDef09.raw and the versions subsequently disclosed and relied upon showed that the changes were to change tenses from the deleted version (which talked about Bitcoin in the past tense) to the future tense in the disclosed versions. This is consistent with the documents being edited to support his case. 339.4. The deleted earlier versions also contained references to papers post-dating the purported 2007 date, such as ‘Xia et Al’ (2016). All such references were deleted and in the disclosed version replaced by a ‘?’ – clearly to indicate that a reference needed to be added. The evident reason that these, and only these, references were removed, is to remove what would otherwise have been obvious indications that the document post-dated 2007. 339.5. It is clear that Dr Wright did use ChatGPT and these documents included classic indicia of how ChatGPT formats references (set out above). 339.6. Mr Lynch agreed with Mr Madden that ID_0004722 and ID_004723 were manipulated {Q/6/5}. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

Satoshi or Sham? The Dubious Nature of Craig Wright's LaTeX Documents

Evolution of Data Sharding Towards Automation and Flexibility in Apache Doris

অ্যাপাচি ডরিসে অটোমেশন এবং নমনীয়তার দিকে ডেটা ভাগ করার বিবর্তন

Entwicklung des Data Sharding hin zu Automatisierung und Flexibilität in Apache Doris

Evolución de la fragmentación de datos hacia la automatización y la flexibilidad en Apache Doris

Evolution du partage de données vers l'automatisation et la flexibilité dans Apache Doris

अपाचे डोरिस में स्वचालन और लचीलेपन की ओर डेटा शार्डिंग का विकास

Apache Doris における自動化と柔軟性に向けたデータ シャーディングの進化

Apache Doris의 자동화 및 유연성을 향한 데이터 샤딩의 진화

Evolução da fragmentação de dados em direção à automação e flexibilidade no Apache Doris

Эволюция сегментирования данных в сторону автоматизации и гибкости в Apache Doris

Apache Doris'te Veri Parçalamanın Otomasyona ve Esnekliğe Doğru Evrimi

Sự phát triển của việc phân chia dữ liệu theo hướng tự động hóa và linh hoạt trong Apache Doris

Apache Doris 中数据分片向自动化和灵活性的演变

Supreme Court Justice Barrett's Opinion on Trump v United States

Operationalizing Diversity and Inclusion Requirements for AI Systems: Results 

Does the U.S. President Have Full Immunity? Here's What the U.S. Supreme Court Had to Say

Trump v. United States: All of the Important Summary Points

Mastering Dynamic Web Scraping

ডায়নামিক ওয়েব স্ক্র্যাপিং আয়ত্ত করা

Dynamisches Web Scraping beherrschen

Dominar el raspado web dinámico

Maîtriser le Web Scraping dynamique

गतिशील वेब स्क्रैपिंग में निपुणता

動的ウェブスクレイピングをマスターする

동적 웹 스크래핑 마스터하기

Dominando o Web Scraping Dinâmico

Освоение динамического парсинга веб-страниц

Dinamik Web Scraping'de Uzmanlaşmak

Làm chủ việc quét web động

掌握动态网页抓取

Real-Time Data Processing with Kafka Streams: Simplifying Stream Processing Applications

Survey and Analysis of Smart Contract Quality Assurance: Defense Methodologies

Author:
(1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction
Dancing Links
The Rust Programming Language
RAC: Hardware/Software Co-Assurance at Scale
Rust and RAR
Dancing Links in Rust
Related Work
Conclusion, Acknowledgments, and References 6 Dancing Links in Rust In this section, we describe an array-based circular doubly-linked list (CDLL) employing Knuth’s “Dancing Links” optimization, realized using our RAR Rust subset. The CDLL data structure implementation constitutes over 700 lines of Rust code, which becomes 890 lines of code when translated to ACL2. 6.1 Definitions First, we present the basic RAR declaration for the CDLL. Rust data structure declarations are similar to those in C, but struct elements are declared by specifying the element name, followed by the : separator, then the element type. Also note that Rust pragmas may be given using the derive attribute. In the declaration above, the array nodeArr holds the list element nodes. Each element has next and prev indices. Note that indices in Rust are normally declared to be of the usize type. Note also that by using array indices instead of references, we get around Rust ownership model issues with circular data structures. The alloc field of the CDLLNode structure is declared to be a two bit unsigned field, but its only allowed values are two non-zero values: 2 (not currently allocated), and 3 (allocated). The reason for this has to do with the details of ACL2 untyped record reasoning, which will be discussed in Section 6.2. The Dancing Links operators cdll_remove and cdll_restore are presented in Figures 4 and 5, respectively. Rust functions begin with the fn keyword, followed by the function name, a parenthesized list of parameters, the -> (returns) symbol, the return type name, followed by the function body (delimited by a curly brace pair). A function parameter list element consists of the parameter name, the : symbol, then the parameter type. Additional parameter modifiers, for example mut, may be present to indicate that the parameter is changed in the function body. Within the function body, the syntax is similar to other C-like languages, but local variable declarations begin with let, and use the variable name, :, variable type declaration syntax. A local variable declaration may also require the mut modifier if that local variable is updated after its initialization. 6.2 Translation to ACL2 We use Plexi to transpile the RAR source to RAC (not shown), then use the RAC translator to convert the resulting RAC source to ACL2. The translation of cdll_restore() appears in Fig. 6. The first thing to note about Fig. 6 is that, even though we are two translation steps away from the original RAR source, the translated function is nonetheless quite readable, which is a rare thing for machine-generated code. Another notable observation is that struct and array ‘get’ and ‘set’ operations become untyped record operators, AG and AS, respectively — these are slight RAC-specific customizations of the usual ACL2 untyped record operators. Further, IF1 is a RAC-specific macro, and LOG>, LOG=, LOG<, and LOGIOR1 are all RTL functions. Thus, much of the proof effort involved with RAR code is reasoning about untyped records and RTL — although not a lot of RTL-specific knowledge is needed, at least in our experience. One aspect of untyped records that can be tricky is that record elements that take on the default value are not explicitly stored in the association list for the record. For RAC untyped records, that default value is zero. Thus, it is easy for a given record to attain a nil value. When reasoning about arrays of such records, it is often desirable to be able to state that the array size remains constant. Thus, for example, for the CDLL array nodeArr of Section 6.1, we ensure that all CDLLNode elements of that array are non-nil by making sure that the alloc fields of the CDLLNode elements are always non-zero (2 or 3). 6.3 Dancing Links Theorems Once we have translated the circular doubly-linked list functions into ACL2, we can begin to prove theorems about the data structure implementation. We begin by defining a “well-formedness” predicate for CDLLs. Given this definition of a good CDLL state, we can prove functional correctness theorems for Dancing Links operations, of the sort stated below. Note that this proof requires some detailed well-formedness hypotheses related to the prev and next indices for the nth element: ACL2 performs the correctness proof for this cdll_restore of cdll_remove theorem automatically. In addition to the Dancing Links operator proofs, we have proved approximately 160 theorems related to the CDLL data structure, including theorems about cdll_cns() (cons equivalent), cdll_- rst() (cdr equivalent), cdll_snc() (add to end of data structure), cdll_tsr() (delete from end of data structure), cdll_nth(), etc. All of these proofs will be made publicly available in the ACL2 workshop books repository. This paper is available on arxiv under CC 4.0 license. Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Author: Author: (1) David S. Hardin, Cedar Rapids, IA USA david.s.hardin@gmail.com. Table of Links Abstract and Introduction Dancing Links The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Dancing Links in Rust Related Work Conclusion, Acknowledgments, and References Abstract and Introduction Abstract and Introduction Dancing Links Dancing Links The Rust Programming Language The Rust Programming Language RAC: Hardware/Software Co-Assurance at Scale RAC: Hardware/Software Co-Assurance at Scale Rust and RAR Rust and RAR Dancing Links in Rust Dancing Links in Rust Related Work Related Work Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 6 Dancing Links in Rust In this section, we describe an array-based circular doubly-linked list (CDLL) employing Knuth’s “Dancing Links” optimization, realized using our RAR Rust subset. The CDLL data structure implementation constitutes over 700 lines of Rust code, which becomes 890 lines of code when translated to ACL2. 6.1 Definitions First, we present the basic RAR declaration for the CDLL. Rust data structure declarations are similar to those in C, but struct elements are declared by specifying the element name, followed by the : separator, then the element type. Also note that Rust pragmas may be given using the derive attribute. In the declaration above, the array nodeArr holds the list element nodes. Each element has next and prev indices. Note that indices in Rust are normally declared to be of the usize type. Note also that by using array indices instead of references, we get around Rust ownership model issues with circular data structures. The alloc field of the CDLLNode structure is declared to be a two bit unsigned field, but its only allowed values are two non-zero values: 2 (not currently allocated), and 3 (allocated). The reason for this has to do with the details of ACL2 untyped record reasoning, which will be discussed in Section 6.2. The Dancing Links operators cdll_remove and cdll_restore are presented in Figures 4 and 5, respectively. Rust functions begin with the fn keyword, followed by the function name, a parenthesized list of parameters, the -> (returns) symbol, the return type name, followed by the function body (delimited by a curly brace pair). A function parameter list element consists of the parameter name, the : symbol, then the parameter type. Additional parameter modifiers, for example mut, may be present to indicate that the parameter is changed in the function body. Within the function body, the syntax is similar to other C-like languages, but local variable declarations begin with let, and use the variable name, :, variable type declaration syntax. A local variable declaration may also require the mut modifier if that local variable is updated after its initialization. 6.2 Translation to ACL2 We use Plexi to transpile the RAR source to RAC (not shown), then use the RAC translator to convert the resulting RAC source to ACL2. The translation of cdll_restore() appears in Fig. 6. The first thing to note about Fig. 6 is that, even though we are two translation steps away from the original RAR source, the translated function is nonetheless quite readable, which is a rare thing for machine-generated code. Another notable observation is that struct and array ‘get’ and ‘set’ operations become untyped record operators, AG and AS, respectively — these are slight RAC-specific customizations of the usual ACL2 untyped record operators. Further, IF1 is a RAC-specific macro, and LOG>, LOG=, LOG<, and LOGIOR1 are all RTL functions. Thus, much of the proof effort involved with RAR code is reasoning about untyped records and RTL — although not a lot of RTL-specific knowledge is needed, at least in our experience. One aspect of untyped records that can be tricky is that record elements that take on the default value are not explicitly stored in the association list for the record. For RAC untyped records, that default value is zero. Thus, it is easy for a given record to attain a nil value. When reasoning about arrays of such records, it is often desirable to be able to state that the array size remains constant. Thus, for example, for the CDLL array nodeArr of Section 6.1, we ensure that all CDLLNode elements of that array are non-nil by making sure that the alloc fields of the CDLLNode elements are always non-zero (2 or 3). 6.3 Dancing Links Theorems Once we have translated the circular doubly-linked list functions into ACL2, we can begin to prove theorems about the data structure implementation. We begin by defining a “well-formedness” predicate for CDLLs. Given this definition of a good CDLL state, we can prove functional correctness theorems for Dancing Links operations, of the sort stated below. Note that this proof requires some detailed well-formedness hypotheses related to the prev and next indices for the nth element: ACL2 performs the correctness proof for this cdll_restore of cdll_remove theorem automatically. In addition to the Dancing Links operator proofs, we have proved approximately 160 theorems related to the CDLL data structure, including theorems about cdll_cns() (cons equivalent), cdll_- rst() (cdr equivalent), cdll_snc() (add to end of data structure), cdll_tsr() (delete from end of data structure), cdll_nth(), etc. All of these proofs will be made publicly available in the ACL2 workshop books repository. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Verification of a Rust Implementation of Knuth’s Dancing Links Using ACL2: Dancing Links in Rust

OpenCitations Meta: Methodology

OpenCitations Meta: পদ্ধতি

OpenCitations Meta: Methodik

Meta de OpenCitations: Metodología

Méta OpenCitations : Méthodologie

ओपनसाइटेशन्स मेटा: कार्यप्रणाली

OpenCitations メタ: 方法論

OpenCitations Meta: 방법론

OpenCitations Meta: Metodologia

Мета OpenCitations: методология

OpenCitations Metası: Metodoloji

OpenCites Meta: Phương pháp luận

OpenCitations Meta：方法论

Authors:
(1) Kexun Zhang, UC Santa Barbara and Equal contribution;
(2) Hongqiao Chen, Northwood High School and Equal contribution;
(3) Lei Li, Carnegie Mellon University;
(4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro
Related Work
ToolDec: LLM Tool Use via Finite-State Decoding
Experiment: ToolDec Eliminates Syntax Errors
Experiment: ToolDec Enables Generalizable Tool Selection
Conclusion and References
Appendix 6. CONCLUSION This paper presents TOOLDEC, a novel decoding algorithm designed to enhance Large Language Models (LLMs) by integrating external tools and ensuring their invocation is syntax-error-free. TOOLDEC, guided by a finite-state machine constructed from tool documentation and API signatures, accurately represents the grammar of tool calls, addressing prevalent issues like erroneous tool calls and poor generalization to unseen tools in existing models. Experiments demonstrate that TOOLDEC eliminates tool-related syntax errors, improves accuracy, and saves inference time across various benchmarks. It also exhibits the ability to generalize to unseen tools in multiple domains without additional fine-tuning data. The advancements by TOOLDEC open avenues for research in developing more sophisticated models adaptable to a wider range of tools and applications without additional training data, leading to more versatile and robust LLMs capable of solving a broader spectrum of complex problems. The success of TOOLDEC in eliminating syntax errors can inspire research focusing on semantic accuracy and contextual relevance of tool calls. This can lead to models that invoke, understand, and leverage tools more effectively, enhancing LLMs’ overall problem-solving capabilities. REFERENCES Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 936–945, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v1/D17-1098. URL https://aclanthology.org/D17-1098. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, pp. 2206–2240. PMLR, 2022. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv e-prints, pp. arXiv–2211, 2022. Jason Eisner. Parameter estimation for probabilistic finite-state transducers. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 1–8, 2002. Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pp. 10764–10799. PMLR, 2023. Tanmay Gupta and Aniruddha Kembhavi. Visual programming: Compositional visual reasoning without training. ArXiv, abs/2211.11559, 2022. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929–3938. PMLR, 2020. Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. arXiv preprint arXiv:2305.11554, 2023. Chris Hokamp and Qun Liu. Lexically constrained decoding for sequence generation using grid beam search. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1535–1546, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1141. URL https:// aclanthology.org/P17-1141. Jan-Christoph Kalo and Leandra Fichtel. Kamel : Knowledge analysis with multitoken entities in language models. Automated Knowledge Base Construction, 2022. Li Li, Wu Chou, Wei Zhou, and Min Luo. Design patterns and extensibility of rest api for networking applications. IEEE Transactions on Network and Service Management, 13(1):154–167, 2016. Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Neurologic decoding:(un) supervised neural text generation with predicate logic constraints. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4288–4299, 2021. Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, et al. Neurologic a* esque decoding: Constrained text generation with lookahead heuristics. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 780–799, 2022. Gregoire Mialon, Roberto Dess ´ `ı, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. Augmented ` language models: a survey. arXiv preprint arXiv:2302.07842, 2023. Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li. Cgmh: Constrained sentence generation by metropolis-hastings sampling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 6834–6842, 2019. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022. Aaron Parisi, Yao Zhao, and Noah Fiedel. Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255, 2022. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Pushpendre Rastogi, Ryan Cotterell, and Jason Eisner. Weighting finite-state transductions with neural context. In Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp. 623–633, 2016. Timo Schick, Jane Dwivedi-Yu, Roberto Dess`ı, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023. Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, and Sujian Li. Restgpt: Connecting large language models with real-world restful apis, 2023. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- ` mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Authors: Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Appendix Abstract and Intro Abstract and Intro Related Work Related Work ToolDec: LLM Tool Use via Finite-State Decoding ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Conclusion and References Appendix Appendix 6. CONCLUSION This paper presents TOOLDEC, a novel decoding algorithm designed to enhance Large Language Models (LLMs) by integrating external tools and ensuring their invocation is syntax-error-free. TOOLDEC, guided by a finite-state machine constructed from tool documentation and API signatures, accurately represents the grammar of tool calls, addressing prevalent issues like erroneous tool calls and poor generalization to unseen tools in existing models. Experiments demonstrate that TOOLDEC eliminates tool-related syntax errors, improves accuracy, and saves inference time across various benchmarks. It also exhibits the ability to generalize to unseen tools in multiple domains without additional fine-tuning data. The advancements by TOOLDEC open avenues for research in developing more sophisticated models adaptable to a wider range of tools and applications without additional training data, leading to more versatile and robust LLMs capable of solving a broader spectrum of complex problems. The success of TOOLDEC in eliminating syntax errors can inspire research focusing on semantic accuracy and contextual relevance of tool calls. This can lead to models that invoke, understand, and leverage tools more effectively, enhancing LLMs’ overall problem-solving capabilities. REFERENCES Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 936–945, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v1/D17-1098. URL https://aclanthology.org/D17-1098. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, pp. 2206–2240. PMLR, 2022. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv e-prints, pp. arXiv–2211, 2022. Jason Eisner. Parameter estimation for probabilistic finite-state transducers. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 1–8, 2002. Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pp. 10764–10799. PMLR, 2023. Tanmay Gupta and Aniruddha Kembhavi. Visual programming: Compositional visual reasoning without training. ArXiv, abs/2211.11559, 2022. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929–3938. PMLR, 2020. Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. arXiv preprint arXiv:2305.11554, 2023. Chris Hokamp and Qun Liu. Lexically constrained decoding for sequence generation using grid beam search. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1535–1546, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1141. URL https:// aclanthology.org/P17-1141. Jan-Christoph Kalo and Leandra Fichtel. Kamel : Knowledge analysis with multitoken entities in language models. Automated Knowledge Base Construction, 2022. Li Li, Wu Chou, Wei Zhou, and Min Luo. Design patterns and extensibility of rest api for networking applications. IEEE Transactions on Network and Service Management, 13(1):154–167, 2016. Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Neurologic decoding:(un) supervised neural text generation with predicate logic constraints. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4288–4299, 2021. Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, et al. Neurologic a* esque decoding: Constrained text generation with lookahead heuristics. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 780–799, 2022. Gregoire Mialon, Roberto Dess ´ `ı, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. Augmented ` language models: a survey. arXiv preprint arXiv:2302.07842, 2023. Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li. Cgmh: Constrained sentence generation by metropolis-hastings sampling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 6834–6842, 2019. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022. Aaron Parisi, Yao Zhao, and Noah Fiedel. Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255, 2022. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Pushpendre Rastogi, Ryan Cotterell, and Jason Eisner. Weighting finite-state transductions with neural context. In Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp. 623–633, 2016. Timo Schick, Jane Dwivedi-Yu, Roberto Dess`ı, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023. Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, and Sujian Li. Restgpt: Connecting large language models with real-world restful apis, 2023. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- ` mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Syntax Error-Free and Generalizable Tool Use for LLMs: Conclusion and References

এলএলএম-এর জন্য সিনট্যাক্স ত্রুটি-মুক্ত এবং সাধারণীকরণযোগ্য টুল ব্যবহার: উপসংহার এবং রেফারেন্স

Syntaxfehlerfreier und generalisierbarer Tooleinsatz für LL.M.: Fazit und Referenzen

Uso de herramientas generalizables y sin errores de sintaxis para LLM: conclusiones y referencias

Utilisation d'outils de syntaxe sans erreur et généralisables pour les LLM : conclusion et références

एलएलएम के लिए वाक्यविन्यास त्रुटि-मुक्त और सामान्यीकरण योग्य उपकरण का उपयोग: निष्कर्ष और संदर्भ

LLM における構文エラーのない一般化可能なツールの使用: 結論と参考文献

LLM을 위한 구문 오류가 없고 일반화 가능한 도구 사용: 결론 및 참조

Uso de ferramenta generalizável e livre de erros de sintaxe para LLMs: conclusão e referências

Использование без синтаксических ошибок и обобщения инструментов для программ LLM: выводы и ссылки

Yüksek Lisans'lar için Sözdizimi Hatasız ve Genelleştirilebilir Araç Kullanımı: Sonuç ve Referanslar

Sử dụng công cụ không có lỗi và có thể khái quát hóa cho LLM: Kết luận và tài liệu tham khảo

语法无错误且可推广的法学硕士工具使用：结论和参考文献

Tailoring Textual Resources for Evaluation Tasks: Long-Form Text Summarization

SEO Clerks is an absolute disaster zone for anyone seeking legitimate digital marketing services. If you enjoy flushing your hard-earned money down the virtual toilet, then by all means, dive headfirst into the scam cesspool that is SEO Clerks. Let's start with the quality of services offered. Spoiler alert: there is none. It's like entering a virtual black market where snake oil peddlers roam freely, offering promises of boosting your website's ranking overnight. But what do you get in return? Absolutely nothing but a dent in your wallet and a bruise to your pride. Oh, but it gets worse. Brace yourself for the horror show of link building scams. You'll find more shady backlinks here than in a cyberpunk novel. These so-called "experts" promise to catapult your website to the top of Google's search results with their magic links. What they don't tell you is that those links are about as useful as a screen door on a submarine. Customer service? Ha! Don't make me laugh. Trying to get a refund from SEO Clerks is like trying to squeeze water from a stone. Once they have your money, they vanish into the digital ether faster than you can say "rip-off." And let's not forget the cherry on top: the user interface straight out of the early 2000s. Navigating SEO Clerks feels like stumbling through a labyrinth designed by a sadistic web designer who enjoys watching people suffer. Imagine stumbling upon a digital marketing marketplace that boasts more scams than a back alley in a cyberpunk dystopia. Welcome to SEO Clerks, where the promise of boosting your website's ranking is about as trustworthy as a politician's campaign pledge. Let's dissect this mess, shall we? First off, the quality of services offered here is as genuine as a three-dollar bill. It's like entering a virtual snake pit where every seller is vying to offload their dubious SEO concoctions onto unsuspecting buyers. But wait, it gets juicier. Remember that forum post from Ionicware, the brains behind SEO Clerks, where they play the victim card, blaming banned users for crying scam? Yeah, that's about as believable as a unicorn sighting. It's a classic case of the pot calling the kettle black. Ionicware's sanctimonious declaration only serves to highlight the dumpster fire that is SEO Clerks. Link building scams galore! And let's not forget the pièce de résistance: link building scams galore! You'd have better luck finding a needle in a haystack than a legitimate backlink on this platform. It's a virtual minefield of shady practices that would make even the most hardened internet sleuths break a sweat. A shocking scam example!!! - Come get those Top Page 1 Rankings! https://www.seoclerk.com/Link-Building/5635/ALPHA-SEO-Links-and-Rankings-Booster-Top-Marketplace-Seller An outdated Method from 2014 - Pure SPAM will rank you at the top! I'm absolutely fuming about this guy and his ridiculous SEO link building scam! 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Brace yourself for the horror show of link building scams. You'll find more shady backlinks here than in a cyberpunk novel. These so-called "experts" promise to catapult your website to the top of Google's search results with their magic links. What they don't tell you is that those links are about as useful as a screen door on a submarine. Customer service? Ha! Don't make me laugh. Trying to get a refund from SEO Clerks is like trying to squeeze water from a stone. Once they have your money, they vanish into the digital ether faster than you can say "rip-off." And let's not forget the cherry on top: the user interface straight out of the early 2000s. Navigating SEO Clerks feels like stumbling through a labyrinth designed by a sadistic web designer who enjoys watching people suffer. Imagine stumbling upon a digital marketing marketplace that boasts more scams than a back alley in a cyberpunk dystopia. Welcome to SEO Clerks, where the promise of boosting your website's ranking is about as trustworthy as a politician's campaign pledge. Let's dissect this mess, shall we? First off, the quality of services offered here is as genuine as a three-dollar bill. It's like entering a virtual snake pit where every seller is vying to offload their dubious SEO concoctions onto unsuspecting buyers. But wait, it gets juicier. Remember that forum post from Ionicware , the brains behind SEO Clerks, where they play the victim card, blaming banned users for crying scam? Yeah, that's about as believable as a unicorn sighting. It's a classic case of the pot calling the kettle black. Ionicware's sanctimonious declaration only serves to highlight the dumpster fire that is SEO Clerks. forum post from Ionicware Link building scams galore! Link building scams galore! And let's not forget the pièce de résistance: link building scams galore! You'd have better luck finding a needle in a haystack than a legitimate backlink on this platform. It's a virtual minefield of shady practices that would make even the most hardened internet sleuths break a sweat. A shocking scam example!!! - Come get those Top Page 1 Rankings! https://www.seoclerk.com/Link-Building/5635/ALPHA-SEO-Links-and-Rankings-Booster-Top-Marketplace-Seller https://www.seoclerk.com/Link-Building/5635/ALPHA-SEO-Links-and-Rankings-Booster-Top-Marketplace-Seller An outdated Method from 2014 - Pure SPAM will rank you at the top! I'm absolutely fuming about this guy and his ridiculous SEO link building scam! It's like he's stuck in some digital Stone Age, thinking that spamming web 2.0 URLs with links and then showering them with profile links and forum/blog comment garbage is some kind of magic formula for boosting rankings. Seriously? Are we in 2005? First off, let's talk about web 2.0. Sure, they were hot stuff back when MySpace was cool, but in today's SEO landscape, relying on them as some cornerstone of your strategy is like using a flip phone in the age of smartphones. And embedding them with links? That's like trying to hide a rotten egg under a pile of glitter - it's still a rotten egg, mate! And then there's the profile links and forum/blog comments. Spamming these places with your links is not only lazy, it's downright harmful! Do you really think Google, with all its sophisticated algorithms, is going to look kindly upon your link vomit? Nope, they're more likely to slap you with a penalty faster than you can say "black hat." But what really grinds my gears is that there are still people out there peddling this garbage as some sort of shortcut to SEO success. Newsflash: there are no shortcuts! Building a solid, sustainable SEO strategy takes time, effort, and genuine value creation. It's about crafting quality content, engaging with your audience, and earning links through merit, not manipulation. So to this guy and anyone else trying to pull off this outdated, spammy nonsense: wake up and smell the SEO coffee! The only thing you're building with your scam is a one-way ticket to obscurity in the depths of Google's search results. And frankly, you deserve it. The next Rotten Egg - Pay $400 - Not a single on page technical SEO adjustment!! Ugh, let me tell you about this absolute nightmare of an SEO scammer. This sorry excuse for a human being has the audacity to charge $400 for their so-called "top-ranking" SEO service, and what do they deliver? Nothing but a steaming pile of spammy links. (Useless as tits on a bull) - tits are not used on a bull because bulls do not produce milk. They have the nerve to promise results without doing any real work. No technical optimizations, no on-page technical improvements, no research, and forget about optimizing for Google. Nope, all they do is litter the internet with their garbage links, like a digital fly infestation. And you know what's the worst part? People fall for it! They get sucked into this black hole of deceit, thinking they're investing in their business's success, only to find themselves buried under a mountain of useless backlinks. These scammers don't care about helping your website thrive. They're just out to make a quick buck at the expense of your online reputation. They're like parasites feeding off the desperation of those who don't know any better. SEOClerks and Their Scam-Filled Business Model SEOClerks and Their Scam-Filled Business Model Let's get one thing straight: SEOClerks knows exactly what they're doing. They are fully aware of the scam-riddled marketplace they run and, quite frankly, they don’t care. Why would they? Their entire business model is predicated on making money from freelancing SEO scams. It's infuriating and yet, not surprising. exactly You see, SEOClerks is teeming with shady operators who promise the moon and deliver a pile of dirt. Fake backlinks, bogus traffic, and sketchy SEO services – you name it, they've got it. And it's not like SEOClerks is oblivious to this. No, they are complicit. They actively promote these scams, providing a platform for con artists to thrive. They benefit from transaction fees, and every scam that gets pulled off on their site puts money in their pockets. Honest businesses and individuals looking to improve their online presence get swindled, losing both money and time, while SEOClerks laughs all the way to the bank. It's a disgraceful exploitation of people’s desire to succeed online. SEOClerks isn't just turning a blind eye – they're facilitating and profiting from deception. It's high time someone held them accountable for perpetuating these fraudulent activities. Enough is enough. The freelancing world deserves better than this cesspool of scams masquerading as a legitimate marketplace. SEOClerks Under Fire for Hosting and Promoting SEO Scams Industry insiders and disgruntled customers have accused the platform of knowingly allowing scams to flourish, profiting from deceptive practices that undermine the credibility of freelance SEO work . Industry insiders and disgruntled customers have accused the platform of knowingly allowing scams to flourish, profiting from deceptive practices that undermine the credibility of freelance SEO work Industry insiders and disgruntled customers have accused the platform of knowingly allowing scams to flourish, profiting from deceptive practices that undermine the credibility of freelance SEO work . SEOClerks has become a hub for a variety of SEO services, ranging from backlink building to traffic generation. However, a growing number of reports suggest that many of these services are scams designed to dupe unsuspecting clients. "It's a breeding ground for fake services," said one industry expert. "You have individuals selling non-existent or low-quality backlinks, fake traffic, and other dodgy SEO tricks." The platform operates by taking a commission on each transaction, meaning that they financially benefit from every sale – legitimate or not. This creates a perverse incentive structure where the more scams that are pulled off, the more money SEOClerks makes. Critics argue that this has led to a laissez-faire attitude towards scam listings, with minimal oversight or enforcement of quality standards. Customers have expressed frustration over the lack of accountability. "I paid for high-quality backlinks and got nothing but junk," said one disappointed user. "When I tried to get a refund, I was met with resistance from both the seller and SEOClerks. It’s clear they’re not interested in protecting their customers." Despite numerous complaints and negative reviews, SEOClerks continues to thrive, raising questions about the ethical implications of their business model. "They are well aware of the scams and yet they choose to do nothing," commented another industry insider. "It's all about making money, regardless of the cost to their customers." So, if you ever come across one of these rotten egg SEO scammers, run in the opposite direction as fast as you can. Don't let them sink their claws into your hard-earned money. Your website deserves better than to be tainted by their slimy tactics. If you value your sanity and your wallet, run far, far away from SEO Clerks. Save yourself the headache, the heartache, and the financial ruin. There are plenty of reputable digital marketing platforms out there. SEO Clerks is not one of them, and it never will be. As the backlash grows, SEOClerks faces mounting pressure to clean up its act. Whether they will take meaningful action or continue to turn a blind eye remains to be seen. For now, the platform remains a controversial player in the world of SEO services, under scrutiny for its role in facilitating and profiting from scams.

SeoClerks.com - The Digital Marketing Dumpster Fire Scam You Must Avoid 

SeoClerks.com - ডিজিটাল মার্কেটিং ডাম্পস্টার ফায়ার স্ক্যাম আপনাকে এড়িয়ে চলতে হবে

SeoClerks.com - Der Digital Marketing Dumpster Fire-Betrug, den Sie vermeiden müssen

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SeoClerks.com - 絶対に避けるべきデジタルマーケティングのゴミ箱詐欺

SeoClerks.com - 피해야 할 디지털 마케팅 쓰레기 수거통 화재 사기

SeoClerks.com - O golpe de incêndio na lixeira de marketing digital que você deve evitar

SeoClerks.com — Мошенничество с пожаром в мусорном контейнере цифрового маркетинга, которого следует избегать

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SeoClerks.com - Trò lừa đảo đốt cháy thùng rác tiếp thị kỹ thuật số bạn phải tránh

SeoClerks.com - 您必须避免的数字营销“垃圾箱火灾”骗局

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu);
(2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu)
(3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. Table of Links Abstract and Introduction
Related Work
The NEWSSENSE Framework
Pilot Study
System Overview
Discussion
Conclusion, Limitations and Ethics, and References
A. Appendix: Prompt for Claim Extraction A. Appendix: Prompt for Claim Extraction This is the prompt used for claim extraction from news article sentences: Extract all the claims from a sentence, ignoring extraneous words such as unimportant adverbs. A sentence may contain multiple claims. Each claim should be of the form < subject> <predicate> <object>, and should have the first occurrence of any pronouns replaced by their antecedents. Sentence: "The 3rd and 4th stations all announced that they would be postponed, and the Monaco station was subsequently cancelled." Claim: Monaco station was cancelled. Claim: 4th stations announced they would be postponed. Claim: The 3rd stations announced they would be postponed. Claim: The 4th stations postponed. Claim: The 3rd stations postponed. Sentence: "Lewis Hamilton and Mercedes have once again confirmed themselves as drivers and constructors world champions." Claim: Mercedes confirmed themselves as constructors world champions. Claim: Lewis Hamilton confirmed themselves as drivers world champions. Sentence: "Local organizers in East Palestine, Ohio on Monday said their activism has successfully pressured rail company Norfolk Southern to agree to a limited relocation plan for some residents affected by last month’s train derailment, but added they have no intention of backing down from their demand for justice for thousands of people in the area who are struggling in the aftermath of the accident." Claim: Local organizers said their activism has pressured rail company Norfolk Southern to agree to a limited relocation plan. Claim: Local organizers have no intention of backing down from their demand for justice. Claim: Rail company Norfolk Southern agree to a limited relocation plan. Sentence: This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu); (2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu) (3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jeremiah Milbauer, Carnegie Mellon University, Pittsburgh PA, USA (email: {jmilbaue | sherryw}@cs.cmu.edu); (2) Ziqi Ding, Carnegie Mellon University, Pittsburgh PA, USA (e-mail: {ziqiding | zhijinw}@andrew.cmu.edu) (3) Tongshuang Wu, Carnegie Mellon University, Pittsburgh PA, USA. Table of Links Abstract and Introduction Related Work The NEWSSENSE Framework Pilot Study System Overview Discussion Conclusion, Limitations and Ethics, and References A. Appendix: Prompt for Claim Extraction Abstract and Introduction Abstract and Introduction Related Work Related Work The NEWSSENSE Framework The NEWSSENSE Framework Pilot Study Pilot Study System Overview System Overview Discussion Discussion Conclusion, Limitations and Ethics, and References Conclusion, Limitations and Ethics, and References A. Appendix: Prompt for Claim Extraction A. Appendix: Prompt for Claim Extraction A. Appendix: Prompt for Claim Extraction This is the prompt used for claim extraction from news article sentences: Extract all the claims from a sentence, ignoring extraneous words such as unimportant adverbs. A sentence may contain multiple claims. Each claim should be of the form < subject> <predicate> <object>, and should have the first occurrence of any pronouns replaced by their antecedents. Sentence: "The 3rd and 4th stations all announced that they would be postponed, and the Monaco station was subsequently cancelled." Claim: Monaco station was cancelled. Claim: 4th stations announced they would be postponed. Claim: The 3rd stations announced they would be postponed. Claim: The 4th stations postponed. Claim: The 3rd stations postponed. Sentence: "Lewis Hamilton and Mercedes have once again confirmed themselves as drivers and constructors world champions." Claim: Mercedes confirmed themselves as constructors world champions. Claim: Lewis Hamilton confirmed themselves as drivers world champions. Sentence: "Local organizers in East Palestine, Ohio on Monday said their activism has successfully pressured rail company Norfolk Southern to agree to a limited relocation plan for some residents affected by last month’s train derailment, but added they have no intention of backing down from their demand for justice for thousands of people in the area who are struggling in the aftermath of the accident." Claim: Local organizers said their activism has pressured rail company Norfolk Southern to agree to a limited relocation plan. Claim: Local organizers have no intention of backing down from their demand for justice. Claim: Rail company Norfolk Southern agree to a limited relocation plan. Sentence:

From Nuisance to News Sense: Appendix: Prompt for Claim Extraction

Next-Gen FHE Toolchains for the Dev Multiverse: How TFHE Is Taking Us There

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FHE-Toolchains der nächsten Generation für das Dev-Multiversum: Wie TFHE uns dorthin bringt

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