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 D Domain-specific pipeline for creating NL instances E Accessing LLMs To access these LLMs, we use the Mistral AI (mistral), and the open-source weights of the remaining models, available at the HuggingFace Hub[5] repositories: • mistralai/Mistral-7B-v0.1 • mistralai/Mistral-7B-Instruct-v0.2 • mistralai/Mixtral-8x7B-Instruct-v0.1 • google/gemma-7b • google/gemma-7b-it • meta-llama/Meta-Llama-3-8B • meta-llama/Meta-Llama-3-8B-Instruct • BioMistral/BioMistral-7B The pretrained LLM weights are used through the transformers[6] python library. All models were loaded with standard configurations and their respective default tokenizers, using the AutoModelForCausalLM and AutoTokenizer classes. Additionally, the models were loaded with the options device_map=“auto", torch_dtype=“auto" attn_implementation=“flash_attention_2" and offload_buffers=True to make the best use of GPU resources available. For the instruction models, the inputs were passed through each model’s chat template (with tokenizer.apply_chat_template), so that they would follow the appropriate prompt format. Responses were cleaned of special symbols for evaluation. Table 3 contains the relevant characteristics of all analyzed models. 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. [5] https://huggingface.co [6] https://huggingface.co/docs/transformers/ 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 D Domain-specific pipeline for creating NL instances E Accessing LLMs To access these LLMs, we use the Mistral AI (mistral), and the open-source weights of the remaining models, available at the HuggingFace Hub[5] repositories: • mistralai/Mistral-7B-v0.1 • mistralai/Mistral-7B-Instruct-v0.2 • mistralai/Mixtral-8x7B-Instruct-v0.1 • google/gemma-7b • google/gemma-7b-it • meta-llama/Meta-Llama-3-8B • meta-llama/Meta-Llama-3-8B-Instruct • BioMistral/BioMistral-7B The pretrained LLM weights are used through the transformers[6] python library. All models were loaded with standard configurations and their respective default tokenizers, using the AutoModelForCausalLM and AutoTokenizer classes. Additionally, the models were loaded with the options device_map=“auto", torch_dtype=“auto" attn_implementation=“flash_attention_2" and offload_buffers=True to make the best use of GPU resources available. For the instruction models, the inputs were passed through each model’s chat template (with tokenizer.apply_chat_template), so that they would follow the appropriate prompt format. Responses were cleaned of special symbols for evaluation. Table 3 contains the relevant characteristics of all analyzed models. 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 [5] https://huggingface.co [6] https://huggingface.co/docs/transformers/

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