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 B Formalization of Tasks 1 and 2 B.1 Task 1: Textual Inference Task Given: A set of premises P = {P1, P2, . . . , Pn} and a conclusion C. Determine whether the logical entailment holds: P |= C. such as (i) The premises and conclusion are instantiated from formal syllogistic schemes and (ii) arguments may be valid or invalid. The assessment is purely on logical validity, independent of the factual correctness of the premises or conclusion. Input: Premises: A set P = {P1, P2, . . . , Pn} of premises, where each Pi is a natural language sentence derived from syllogistic schemes. Conclusion: A natural language sentence C, representing the conclusion. Output: Logical Validity Indicator: A binary output Output ∈ {True, False} such that: Here, P |= C denotes that C is a logical consequence of P. B.2 Task 2: Premise Selection Task Objective: Identify the minimal subset of premises P ′ ⊆ P that are necessary and sufficient to logically derive the conclusion C. • Given: – A set of premises P = {P1, P2, . . . , Pn}. – A conclusion C. • Task Definition: Find the minimal subset P Input: Premises: A set P = {P1, P2, . . . , Pn} containing both relevant and irrelevant (distractor) premises. Conclusion: A natural language sentence C. Output: • Selected Premises: A subset P ′ ⊆ P such that: P ′ |= C, and P ′ is minimal and necessary. 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 B Formalization of Tasks 1 and 2 B.1 Task 1: Textual Inference Task Given: A set of premises P = {P1, P2, . . . , Pn} and a conclusion C. Determine whether the logical entailment holds: P |= C. such as (i) The premises and conclusion are instantiated from formal syllogistic schemes and (ii) arguments may be valid or invalid. The assessment is purely on logical validity, independent of the factual correctness of the premises or conclusion. Input: Input: Premises : A set P = {P1, P2, . . . , Pn} of premises, where each Pi is a natural language sentence derived from syllogistic schemes. Premises Conclusion : A natural language sentence C, representing the conclusion. Conclusion Output: Output: Logical Validity Indicator: A binary output Output ∈ {True, False} such that: Logical Validity Indicator: Here, P |= C denotes that C is a logical consequence of P. B.2 Task 2: Premise Selection Task Objective : Identify the minimal subset of premises P ′ ⊆ P that are necessary and sufficient to logically derive the conclusion C. Objective • Given : Given – A set of premises P = {P1, P2, . . . , Pn}. – A conclusion C. • Task Definition : Find the minimal subset P • Task Definition Input: Input: Premises : A set P = {P1, P2, . . . , Pn} containing both relevant and irrelevant (distractor) premises. Premises Conclusion : A natural language sentence C. Conclusion Output: Output: • Selected Premises : A subset P ′ ⊆ P such that: • Selected Premises P ′ |= C, and P ′ is minimal and necessary. 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

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

How to Test Logical Validity in Biomedical Research

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Formalization of the SylloBio-NLI Resource Generation Process

New Study Reveals AI's Weak Spots in Medical Logic

How AI Models Handle Complex Biomedical Reasoning

How Effective Are AI Models at Biomedical Problem Solving?

Testing AI's Ability to Think Logically: A Breakthrough in Biomedical Research

A Formalization of the SylloBio-NLI Resource Generation Process

New Study Reveals AI's Weak Spots in Medical Logic

How AI Models Handle Complex Biomedical Reasoning

How Effective Are AI Models at Biomedical Problem Solving?

Testing AI's Ability to Think Logically: A Breakthrough in Biomedical Research

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps