mlsanityData to 13 Oct 2026

Survey on Reasoning Capabilities and Accessibility of Large Language Models Using Biology-related Questions

arXiv 2406.16891 ↗First posted 11 May 2024cs.CL cs.AIcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

This research paper discusses the advances made in the past decade in biomedicine and Large Language Models. To understand how the advances have been made hand-in-hand with one another, the paper also discusses the integration of Natural Language Processing techniques and tools into biomedicine. Finally, the goal of this paper is to expand on a survey conducted last year (2023) by introducing a new list of questions and prompts for the top two language models. Through this survey, this paper seeks to quantify the improvement made in the reasoning abilities in LLMs and to what extent those improvements are felt by the average user. Additionally, this paper seeks to extend research on retrieval of biological literature by prompting the LLM to answer open-ended questions in great depth.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 3 of its 9 references that are in the map. First posted on arXiv 11 May 2024.

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3 of 9 references.

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  1. 2023Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions5 Nov

Other references · 2

  1. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
  2. 2023Improving Code Generation by Training with Natural Language Feedbackcited by 75

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  1. 2024A Survey for Large Language Models in Biomedicine
  2. 2023Benchmarking large language models for biomedical natural language processing applications and recommendations
  3. 2024How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions
  4. 2025Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach
  5. 2026From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
  6. 2025RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models
  7. 2025MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation
  8. 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains