Predicate

predicate

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

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables F Experimental Details The entire experimental setup was implemented as a python code package, consisting of 5 modules: • pathways: defines the ontological relations and operations for biological pathways, as well as the logic to retrieve and transform data from the domain ontology. • logic2nl: translates logic formulas into NL statements (premises, conclusions), using parameterised scheme templates. • llm: provides access to LLMs, and facilitates logging. • experiments: defines all test logic, parameterisation and metrics for the experiments. • main: orchestrates all the tests and aggregates results. The LLMs were loaded and run using the HuggingFace transformers library (v4.43.3). The prompts were processed directly by the models with generate, without sampling. Parameters were set as follows: • Number of premises = 2: the number of valid premises per instance (factual argumentative text). • Max distractors = 5: maximum number of distractors to be added per instance. • Subset size = 200: maximum number of positive and negative instances for each scheme. • Batch size = 20: number of instances evaluated simultaneously. • ICL: Whether the in-context learning prompt would be used or not. • Model: the LLM to be evaluated For both Task 1 and Task 2, each model was analyzed across all 28 syllogistic schemes, with responses evaluated from a total of 11,200 instances (28 schemes × (200 positive + 200 negative)). The exceptions were: Generalized Modus Ponens - complex predicates (18 instances), Hypothetical Syllogism 1 - base (202 instances) and Generalized Contraposition - negation (202 instances). This is due to the number of factually possible different instances being limited by the ground-truth data in such cases. For both tasks, this amounted to a total of 211,840 instances, with performance comparisons made between ZS and ICL settings, leading to a total of 423,680 prompts. When examining the impact of distractors, responses were analyzed across five variants of each argument scheme, reflecting different numbers of irrelevant premises (from 1 to 5 distractors). For each scheme and model, this resulted in 1,000 responses (200 prompts per scheme × 5 variants), with the exception of the aforementioned cases. Across all schemes, this totaled 26,480 responses for one model, and 211,840 responses were analyzed for all models for each number of distractors, totaling 1,059,200 responses. Again, comparisons were made between ZS and ICL settings, doubling the previous number. For both Task 1 and Task 2, performance was compared using actual gene entity names versus synthetic names across two selected schemes. Each scheme was tested with 400 instances (200 positive, 200 negative), resulting in a comprehensive analysis of 12,800 instances per task (200 instances × 2 schemes × 2 entity types × 8 models), providing a thorough comparison of model performance with both factual and synthetic gene names. The experiments were run on a computer with an AMD EPYC 7413 24-Core CPU, 128GB of available RAM and 2 × NVIDIA A100-SXM4-80GB GPUs. Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables F Experimental Details The entire experimental setup was implemented as a python code package, consisting of 5 modules: • pathways: defines the ontological relations and operations for biological pathways, as well as the logic to retrieve and transform data from the domain ontology. • logic2nl: translates logic formulas into NL statements (premises, conclusions), using parameterised scheme templates. • llm: provides access to LLMs, and facilitates logging. • experiments: defines all test logic, parameterisation and metrics for the experiments. • main: orchestrates all the tests and aggregates results. The LLMs were loaded and run using the HuggingFace transformers library (v4.43.3). The prompts were processed directly by the models with generate, without sampling. Parameters were set as follows: • Number of premises = 2: the number of valid premises per instance (factual argumentative text). • Max distractors = 5: maximum number of distractors to be added per instance. • Subset size = 200: maximum number of positive and negative instances for each scheme. • Batch size = 20: number of instances evaluated simultaneously. • ICL: Whether the in-context learning prompt would be used or not. • Model: the LLM to be evaluated For both Task 1 and Task 2, each model was analyzed across all 28 syllogistic schemes, with responses evaluated from a total of 11,200 instances (28 schemes × (200 positive + 200 negative)). The exceptions were: Generalized Modus Ponens - complex predicates (18 instances), Hypothetical Syllogism 1 - base (202 instances) and Generalized Contraposition - negation (202 instances). This is due to the number of factually possible different instances being limited by the ground-truth data in such cases. For both tasks, this amounted to a total of 211,840 instances, with performance comparisons made between ZS and ICL settings, leading to a total of 423,680 prompts. When examining the impact of distractors, responses were analyzed across five variants of each argument scheme, reflecting different numbers of irrelevant premises (from 1 to 5 distractors). For each scheme and model, this resulted in 1,000 responses (200 prompts per scheme × 5 variants), with the exception of the aforementioned cases. Across all schemes, this totaled 26,480 responses for one model, and 211,840 responses were analyzed for all models for each number of distractors, totaling 1,059,200 responses. Again, comparisons were made between ZS and ICL settings, doubling the previous number. For both Task 1 and Task 2, performance was compared using actual gene entity names versus synthetic names across two selected schemes. Each scheme was tested with 400 instances (200 positive, 200 negative), resulting in a comprehensive analysis of 12,800 instances per task (200 instances × 2 schemes × 2 entity types × 8 models), providing a thorough comparison of model performance with both factual and synthetic gene names. The experiments were run on a computer with an AMD EPYC 7413 24-Core CPU, 128GB of available RAM and 2 × NVIDIA A100-SXM4-80GB GPUs. Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

Experimental Setup for Evaluating LLM Performance on Biomedical Syllogistic Tasks

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables C Dictionary of gene and pathway membership Reactome[4] (version 88—March 2024) has entries for 11 226 protein-coding genes involved in 15 212 human reactions annotated from 38 549 literature references. These reactions are grouped into 2 698 pathways collected under 29 superpathways (e.g. Immune System) that describe normal cellular functions. Each superpathway is represented as a roughly circular ‘burst,’ with the central node corresponding to the top-level of the Reactome event hierarchy and concentric rings representing increasingly specific levels of the event hierarchy (sub-pathways) (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The relationships between these pathways are captured through parent-child arcs, reflecting the ontological "is-a" relationships. The 29 Reactome superpathways group are each organized as a roughly circular ‘burst’. However, we built the corpus based on one, largest group of pathways called Disease. The central node of the Disease burst corresponds to the uppermost level of the Reactome event hierarchy (Table 2). Concentric rings of nodes around the central node represent successive more specific levels of the event hierarchy (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The arcs connecting nodes between successive rings within a burst represent parent–child (is-a) relationships in the event hierarchy. When a specific pathway is shared by more than one burst, arcs connect its nodes between bursts. A node’s size is proportional to the number of physical entities (proteins, complexes, chemicals) it contains. The dictionary of entity-names and predicates based on Reactome knowledgebase are available on GitHub. Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. [4] https://reactome.org Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables C Dictionary of gene and pathway membership Reactome[4] (version 88—March 2024) has entries for 11 226 protein-coding genes involved in 15 212 human reactions annotated from 38 549 literature references. These reactions are grouped into 2 698 pathways collected under 29 superpathways (e.g. Immune System) that describe normal cellular functions. Each superpathway is represented as a roughly circular ‘burst,’ with the central node corresponding to the top-level of the Reactome event hierarchy and concentric rings representing increasingly specific levels of the event hierarchy (sub-pathways) (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The relationships between these pathways are captured through parent-child arcs, reflecting the ontological "is-a" relationships. The 29 Reactome superpathways group are each organized as a roughly circular ‘burst’. However, we built the corpus based on one, largest group of pathways called Disease. The central node of the Disease burst corresponds to the uppermost level of the Reactome event hierarchy (Table 2). Concentric rings of nodes around the central node represent successive more specific levels of the event hierarchy (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The arcs connecting nodes between successive rings within a burst represent parent–child (is-a) relationships in the event hierarchy. When a specific pathway is shared by more than one burst, arcs connect its nodes between bursts. A node’s size is proportional to the number of physical entities (proteins, complexes, chemicals) it contains. The dictionary of entity-names and predicates based on Reactome knowledgebase are available on GitHub. Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv [4] https://reactome.org

Reactome Knowledgebase: Gene and Pathway Membership in Disease-Related Pathways

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables A Formalization of the SylloBio-NLI Resource Generation Process This appendix formalises the generation process of the syllogistic inference patterns. We start by defining the mains constructs (formal and linguistic artefacts and functions) of the underlying framework: Syllogistic Scheme (S): A logical inference pattern consisting of premises and a conclusion, S = {P1, P2, . . . , Pn, C}, where Pi is premise i and C is the conclusion.


Formal Argument Scheme (σ): Representation of a syllogistic scheme in first-order logic (FOL), σ(S) = {ϕ1, ϕ2, . . . , ϕn, ψ}, where ϕi corresponds to Pi and ψ corresponds to C.


Natural Language Template (τ ): A natural language schema mapping each formula in σ(S) to a sentence template, τ (σ(S)) = {τ1, τ2, . . . , τn, σ}, where τi is the sentence template for ϕi and σ is the sentence template for ψ.


Ontology (O): A domain-specific knowledge base containing entities E and predicates Π, O = {E, Π}, where E = {e1, e2, . . . , ek} and Π = {π1, π2, . . . , πl}.


Instantiation Function (I): A function that replaces placeholders in τ with entities and predicates from O, I : τ (σ(S)) × O → NL, where NL is the set of natural language sentences.


Expert Mapping Function (µExpert): A function provided by a domain expert to map placeholders to appropriate ontology terms, µExpert : Placeholders → E ∪ Π.


Knowledge Base (KB): A collection of instantiated syllogistic arguments, KB = {A1, A2, . . . , Am}, where Ai = {P ′ 1 , P′ 2 , . . . , P′ n , C′} and P ′ i , C′ are instantiated natural language sentences. A.1 Process Formalisation The process formalisation defines a systematic process for generating domain-specific syllogistic arguments by: 1. Defining formal representations of syllogistic schemes in first-order logic. Generating natural language templates from these formal representations.


Mapping placeholders to domain-specific entities and predicates using an ontology and expert knowledge.


Instantiating the templates to produce logically valid and semantically sound arguments.


Constructing a knowledge base for evaluating NLI models. This ensures that the generated arguments are both logically valid and contextually relevant to the biomedical domain. Input: A set of syllogistic schemes: S = {S1, S2, . . . , Sm}, an ontology: O = {E, Π}, an expert mapping function: µExpert. Output: A knowledge base of instantiated arguments: KB. Step 1: Formal Argument Scheme Selection: For each syllogistic scheme Si ∈ S, define its formal argument scheme in first-order logic: Step 2: Natural Language Template Generation: Transform each formula in σ(Si) into a natural language template: Step 3: Ontology Mapping and Instantiation: Apply the expert mapping function to select appropriate entities and predicates from the ontology: Instantiate the templates: under the following constraints: • Logical Validity: The instantiated arguments must preserve the logical structure of σ(Si). • Domain Soundness: The selected entities and predicates must be semantically coherent within the targeted subdomain. These constraints can be further formalised as: Logical Validity Constraint: The instantiated argument Ai must be logically valid: {ϕ ′ 1 , ϕ′ 2 , . . . , ϕ′ n} |= ψ ′ , where ϕ ′ j corresponds to the logical form of P ′ j . Domain Soundness Constraint: The entities and predicates used must be semantically valid within the domain: ∀e ∈ E ′ , π ∈ Π ′ , DomainValid(e, π) = True, where E′ ⊆ E and Π′ ⊆ Π are entities and predicates used in Ai Verification of Logical Validity: Ensure that the instantiated premises logically entail the conclusion: {ϕ ′ 1 , ϕ′ 2 , . . . , ϕ′ n} |= ψ ′ , using logical inference rules. Verification of Domain Soundness: Confirm that: • All entities and predicates are correctly used. • There are no semantic contradictions. Step 4: Knowledge Base Construction: Aggregate all instantiated arguments into the knowledge base: This is summarised with the following algorithmic outline: Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables A Formalization of the SylloBio-NLI Resource Generation Process This appendix formalises the generation process of the syllogistic inference patterns. We start by defining the mains constructs (formal and linguistic artefacts and functions) of the underlying framework: Syllogistic Scheme (S): A logical inference pattern consisting of premises and a conclusion, S = {P1, P2, . . . , Pn, C}, where Pi is premise i and C is the conclusion. Formal Argument Scheme (σ): Representation of a syllogistic scheme in first-order logic (FOL), σ(S) = {ϕ1, ϕ2, . . . , ϕn, ψ}, where ϕi corresponds to Pi and ψ corresponds to C. Natural Language Template (τ ): A natural language schema mapping each formula in σ(S) to a sentence template, τ (σ(S)) = {τ1, τ2, . . . , τn, σ}, where τi is the sentence template for ϕi and σ is the sentence template for ψ. Ontology (O): A domain-specific knowledge base containing entities E and predicates Π, O = {E, Π}, where E = {e1, e2, . . . , ek} and Π = {π1, π2, . . . , πl}. Instantiation Function (I): A function that replaces placeholders in τ with entities and predicates from O, I : τ (σ(S)) × O → NL, where NL is the set of natural language sentences. Expert Mapping Function (µExpert): A function provided by a domain expert to map placeholders to appropriate ontology terms, µExpert : Placeholders → E ∪ Π. Knowledge Base (KB): A collection of instantiated syllogistic arguments, KB = {A1, A2, . . . , Am}, where Ai = {P ′ 1 , P′ 2 , . . . , P′ n , C′} and P ′ i , C′ are instantiated natural language sentences. Syllogistic Scheme (S): A logical inference pattern consisting of premises and a conclusion, S = {P1, P2, . . . , Pn, C}, where Pi is premise i and C is the conclusion. Syllogistic Scheme (S): A logical inference pattern consisting of premises and a conclusion, S = {P1, P2, . . . , Pn, C}, where Pi is premise i and C is the conclusion. Formal Argument Scheme (σ): Representation of a syllogistic scheme in first-order logic (FOL), σ(S) = {ϕ1, ϕ2, . . . , ϕn, ψ}, where ϕi corresponds to Pi and ψ corresponds to C. Formal Argument Scheme (σ): Representation of a syllogistic scheme in first-order logic (FOL), σ(S) = {ϕ1, ϕ2, . . . , ϕn, ψ}, where ϕi corresponds to Pi and ψ corresponds to C. Natural Language Template (τ ): A natural language schema mapping each formula in σ(S) to a sentence template, τ (σ(S)) = {τ1, τ2, . . . , τn, σ}, where τi is the sentence template for ϕi and σ is the sentence template for ψ. Natural Language Template (τ ): A natural language schema mapping each formula in σ(S) to a sentence template, τ (σ(S)) = {τ1, τ2, . . . , τn, σ}, where τi is the sentence template for ϕi and σ is the sentence template for ψ. Ontology (O): A domain-specific knowledge base containing entities E and predicates Π, O = {E, Π}, where E = {e1, e2, . . . , ek} and Π = {π1, π2, . . . , πl}. Ontology (O): A domain-specific knowledge base containing entities E and predicates Π, O = {E, Π}, where E = {e1, e2, . . . , ek} and Π = {π1, π2, . . . , πl}. Instantiation Function (I): A function that replaces placeholders in τ with entities and predicates from O, I : τ (σ(S)) × O → NL, where NL is the set of natural language sentences. Instantiation Function (I): A function that replaces placeholders in τ with entities and predicates from O, I : τ (σ(S)) × O → NL, where NL is the set of natural language sentences. Expert Mapping Function (µExpert): A function provided by a domain expert to map placeholders to appropriate ontology terms, µExpert : Placeholders → E ∪ Π. Expert Mapping Function (µExpert): A function provided by a domain expert to map placeholders to appropriate ontology terms, µExpert : Placeholders → E ∪ Π. Knowledge Base (KB): A collection of instantiated syllogistic arguments, KB = {A1, A2, . . . , Am}, where Ai = {P ′ 1 , P′ 2 , . . . , P′ n , C′} and P ′ i , C′ are instantiated natural language sentences. Knowledge Base (KB): A collection of instantiated syllogistic arguments, KB = {A1, A2, . . . , Am}, where Ai = {P ′ 1 , P′ 2 , . . . , P′ n , C′} and P ′ i , C′ are instantiated natural language sentences. A.1 Process Formalisation The process formalisation defines a systematic process for generating domain-specific syllogistic arguments by: 1. Defining formal representations of syllogistic schemes in first-order logic. Defining Generating natural language templates from these formal representations. Mapping placeholders to domain-specific entities and predicates using an ontology and expert knowledge. Instantiating the templates to produce logically valid and semantically sound arguments. Constructing a knowledge base for evaluating NLI models. Generating natural language templates from these formal representations. Generating natural language templates from these formal representations. Generating Mapping placeholders to domain-specific entities and predicates using an ontology and expert knowledge. Mapping placeholders to domain-specific entities and predicates using an ontology and expert knowledge. Mapping Instantiating the templates to produce logically valid and semantically sound arguments. Instantiating the templates to produce logically valid and semantically sound arguments. Instantiating Constructing a knowledge base for evaluating NLI models. Constructing a knowledge base for evaluating NLI models. Constructing This ensures that the generated arguments are both logically valid and contextually relevant to the biomedical domain. Input : A set of syllogistic schemes: S = {S1, S2, . . . , Sm}, an ontology: O = {E, Π}, an expert mapping function: µExpert. Input Output : A knowledge base of instantiated arguments: KB. Output Step 1 : Formal Argument Scheme Selection: For each syllogistic scheme Si ∈ S, define its formal argument scheme in first-order logic: Step 1 Step 2: Natural Language Template Generation : Transform each formula in σ(Si) into a natural language template: Step 2: Natural Language Template Generation Step 3: Ontology Mapping and Instantiation : Apply the expert mapping function to select appropriate entities and predicates from the ontology: Step 3: Ontology Mapping and Instantiation Instantiate the templates: under the following constraints: • Logical Validity : The instantiated arguments must preserve the logical structure of σ(Si). • Logical Validity • Domain Soundness: The selected entities and predicates must be semantically coherent within the targeted subdomain. • Domain Soundness: These constraints can be further formalised as: Logical Validity Constraint : The instantiated argument Ai must be logically valid: Logical Validity Constraint {ϕ ′ 1 , ϕ′ 2 , . . . , ϕ′ n} |= ψ ′ , where ϕ ′ j corresponds to the logical form of P ′ j . Domain Soundness Constraint: The entities and predicates used must be semantically valid within the domain: Domain Soundness Constraint: ∀e ∈ E ′ , π ∈ Π ′ , DomainValid(e, π) = True, where E′ ⊆ E and Π′ ⊆ Π are entities and predicates used in Ai Verification of Logical Validity : Ensure that the instantiated premises logically entail the conclusion: Verification of Logical Validity {ϕ ′ 1 , ϕ′ 2 , . . . , ϕ′ n} |= ψ ′ , using logical inference rules. Verification of Domain Soundness : Confirm that: Verification of Domain Soundness • All entities and predicates are correctly used. • There are no semantic contradictions. Step 4: Knowledge Base Construction : Aggregate all instantiated arguments into the knowledge base: Step 4: Knowledge Base Construction This is summarised with the following algorithmic outline: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

A Formalization of the SylloBio-NLI Resource Generation Process

How Effective Are AI Models at Biomedical Problem Solving?

How AI Models Handle Complex Biomedical Reasoning

New Study Reveals AI's Weak Spots in Medical Logic

Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 4 WORKLOAD CHARACTERIZATION The initial step in the tuning system involves modeling the characteristics of the target workload to facilitate the tuning algorithm in acquiring knowledge from historical data, thereby accelerating the tuning process or improving coverage to achieve optimal configurations. In this section, we present two main aspects for modeling DBMS workloads: a query-level characterization based on the queries composed in a workload and a runtime-based characterization based on DBMS runtime statistics obtained during the execution of the instance. 4.1 Query-level Characterization Query-level characterization aims at modeling a workload from its composed queries [3]. A DBMS application allows users and other applications to engage with the database by formulating and executing queries, such as SELECT, INSERT, UPDATE, DELETE—tailored to manipulate and retrieve information and accomplish various tasks. Hence, researchers have endeavored to model a workload by analyzing the composition of queries and utilizing query characteristics to represent the workload [3]. Existing work on query-level characterization can be summarized as follows: • Features from the query text: In a DBMS workload, each query will perform a certain type of operation within the DBMS. Hence, some researchers try to characterize a workload by analyzing its static query text and then merging each query vector to ultimately represent the workload. RestTune [6] analyzes the cost of a MySQL workload by examining the distribution of queries within each workload based on their reserved SQL keywords (e.g., SELECT, UPDATE, DISTINCT). Initially, it identifies the reserved operation keywords for each query and subsequently computes their TF-IDF (term frequency-inverse document frequency) values. These TF-IDF feature vectors serve as input for a random forest model utilized for classification. This model provides a predicted probability distribution regarding predefined resource cost levels for each query. Ultimately, the average of the probability distributions for all queries across the entire workload is calculated to represent the metafeature cost for the input workload. LOCAT [7] features a Spark SQL workload directly with its query composition. It then filters out some unimportant queries and uses those important queries to represent a workload when evaluating the performance of recommended configuration. ONLINETUNE [3] features a workload in terms of the query arrival rate within it and its query composition. It leverages a standard LSTM encoder-decoder network to encode each query in the workload. Subsequently, it averages the query encodings to obtain the composition feature of the workload. • Features from the query plan: In addition to the static query text, some researchers model a query through looking into its execution process. When a query is submitted to the DBMS, the system first parses the query to check its syntax and semantics. After parsing, the query is compiled into an execution plan, which outlines the steps needed to execute the query efficiently. Some researchers represent a query through parsing its query plan to aggregate key features, including the cost or categories of operators, scanned tables, and predicates, and then featurizing the workload with the query vectors. In Qtune [9], each SQL query is represented with features including query type (e.g., insert, delete, select, update), involved tables, and the estimated processing cost derived from inherent operations (e.g., scan, hash join, aggregate). Then, it merges all the query vectors into a unified workload vector according to its predefined merging strategies for each part. • Features from other query-related factors. In addition to the aforementioned query features, certain studies consider the underlying data distribution in the database as an informative factor, based on the observation that only data changes affecting the workload queries are relevant to the tuning policy. Building upon this premise, ONLINETUNE [3] extracts the database data distribution by leveraging insights from the DBMS optimizer, which encompass estimates of rows scrutinized by queries, the proportion of rows filtered by table conditions in queries, and the utilization of indexes. Subsequently, it computes the average of these three query features to derive the underlying data distribution associated with a workload. Finally, combined with the features extracted from query text, it concatenates the workload feature and underlying data feature to obtain the final contextual features. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 4 WORKLOAD CHARACTERIZATION The initial step in the tuning system involves modeling the characteristics of the target workload to facilitate the tuning algorithm in acquiring knowledge from historical data, thereby accelerating the tuning process or improving coverage to achieve optimal configurations. In this section, we present two main aspects for modeling DBMS workloads: a query-level characterization based on the queries composed in a workload and a runtime-based characterization based on DBMS runtime statistics obtained during the execution of the instance. 4.1 Query-level Characterization Query-level characterization aims at modeling a workload from its composed queries [3]. A DBMS application allows users and other applications to engage with the database by formulating and executing queries, such as SELECT, INSERT, UPDATE, DELETE—tailored to manipulate and retrieve information and accomplish various tasks. Hence, researchers have endeavored to model a workload by analyzing the composition of queries and utilizing query characteristics to represent the workload [3]. Existing work on query-level characterization can be summarized as follows: • Features from the query text: In a DBMS workload, each query will perform a certain type of operation within the DBMS. Hence, some researchers try to characterize a workload by analyzing its static query text and then merging each query vector to ultimately represent the workload. RestTune [6] analyzes the cost of a MySQL workload by examining the distribution of queries within each workload based on their reserved SQL keywords (e.g., SELECT, UPDATE, DISTINCT). Initially, it identifies the reserved operation keywords for each query and subsequently computes their TF-IDF (term frequency-inverse document frequency) values. These TF-IDF feature vectors serve as input for a random forest model utilized for classification. This model provides a predicted probability distribution regarding predefined resource cost levels for each query. Ultimately, the average of the probability distributions for all queries across the entire workload is calculated to represent the metafeature cost for the input workload. LOCAT [7] features a Spark SQL workload directly with its query composition. It then filters out some unimportant queries and uses those important queries to represent a workload when evaluating the performance of recommended configuration. ONLINETUNE [3] features a workload in terms of the query arrival rate within it and its query composition. It leverages a standard LSTM encoder-decoder network to encode each query in the workload. Subsequently, it averages the query encodings to obtain the composition feature of the workload. • Features from the query text: • Features from the query plan: In addition to the static query text, some researchers model a query through looking into its execution process. When a query is submitted to the DBMS, the system first parses the query to check its syntax and semantics. After parsing, the query is compiled into an execution plan, which outlines the steps needed to execute the query efficiently. Some researchers represent a query through parsing its query plan to aggregate key features, including the cost or categories of operators, scanned tables, and predicates, and then featurizing the workload with the query vectors. In Qtune [9], each SQL query is represented with features including query type (e.g., insert, delete, select, update), involved tables, and the estimated processing cost derived from inherent operations (e.g., scan, hash join, aggregate). Then, it merges all the query vectors into a unified workload vector according to its predefined merging strategies for each part. • Features from the query plan: • Features from other query-related factors. In addition to the aforementioned query features, certain studies consider the underlying data distribution in the database as an informative factor, based on the observation that only data changes affecting the workload queries are relevant to the tuning policy. Building upon this premise, ONLINETUNE [3] extracts the database data distribution by leveraging insights from the DBMS optimizer, which encompass estimates of rows scrutinized by queries, the proportion of rows filtered by table conditions in queries, and the utilization of indexes. Subsequently, it computes the average of these three query features to derive the underlying data distribution associated with a workload. Finally, combined with the features extracted from query text, it concatenates the workload feature and underlying data feature to obtain the final contextual features. • Features from other query-related factors. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

Tuning DBMS: The Secret Life of Queries and Runtime Data

10 Reasons Why Apache Iceberg Will Dominate Data Lakehouses in 2025

Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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