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 J Results: Misaligned Instruction-Response We observed four types of text outputs: those aligned with the instruction (regardless of correctness), empty outputs where no text was generated, incorrect text outputs such as repeated prompts or random content, and outputs resembling Chain-of-Thought (CoT) reasoning that, while potentially containing correct reasoning, did not align with the given instructions (Figs. 8-11). We noticed that BioMistral-7B generated empty outputs in 100% of the cases regardless of the specific settings, while Meta-Llama-3-8B exhibits this behaviour for ZS settings in both tasks. We attribute this observation to safety mechanisms applied during pre-training Labrak et al. [2024], suggesting that domain-specific knowledge about human genome pathways is absent in both models. Similarly, Mistral-7B-v0.1 responses simply repeat the prompt text in 88% of the cases in the ZS settings, and 69% of the cases in FS (Table 1). Moreover, CoT outputs including phrases like e.g. "A nice logical puzzle! Let’s break it down step by step..." were particularly common for Meta-Llama-3-8B Instruct, which often ignored the specific instructions to address the task. This behaviour highlights potential biases introduced during instruction-tuning which make the models unable to generalise to domains that are out-of-distribution of the training set. 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 J Results: Misaligned Instruction-Response We observed four types of text outputs: those aligned with the instruction (regardless of correctness), empty outputs where no text was generated, incorrect text outputs such as repeated prompts or random content, and outputs resembling Chain-of-Thought (CoT) reasoning that, while potentially containing correct reasoning, did not align with the given instructions (Figs. 8-11). We noticed that BioMistral-7B generated empty outputs in 100% of the cases regardless of the specific settings, while Meta-Llama-3-8B exhibits this behaviour for ZS settings in both tasks. We attribute this observation to safety mechanisms applied during pre-training Labrak et al. [2024], suggesting that domain-specific knowledge about human genome pathways is absent in both models. Similarly, Mistral-7B-v0.1 responses simply repeat the prompt text in 88% of the cases in the ZS settings, and 69% of the cases in FS (Table 1). Moreover, CoT outputs including phrases like e.g. "A nice logical puzzle! Let’s break it down step by step..." were particularly common for Meta-Llama-3-8B Instruct, which often ignored the specific instructions to address the task. This behaviour highlights potential biases introduced during instruction-tuning which make the models unable to generalise to domains that are out-of-distribution of the training set. 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

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