Supplementary Figures and Supplementary Tables

Written by largemodels | Published 2024/12/16
Tech Story Tags: syllobio-nli | natural-language-inference | large-language-models-(llms) | syllogistic-reasoning | biomedical-ontologies | evidence-extraction | syllogistic-schemes | zero-shot-learning-(zs)

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Table of Links

  1. Abstract and Introduction
  2. SylloBio-NLI
  3. Empirical Evaluation
  4. Related Work
  5. Conclusions
  6. 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

M Supplementary Figures

N Supplementary Tables

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.


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