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

arXiv: https://arxiv.org/abs/2406.16891 · HTML: https://mlsanity.com/paper/2406.16891/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 3 of 3)

- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [Improving Code Generation by Training with Natural Language Feedback](https://mlsanity.com/paper/2303.16749/) (2023)
- [Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions](https://mlsanity.com/paper/2311.07582/) (2023)

## 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.