CLAIMay 11, 2024

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

arXiv:2406.16891v1
Originality Synthesis-oriented
AI Analysis

It addresses the need to assess how advancements in LLMs benefit users in biomedicine, though it appears incremental as an extension of prior survey work.

This paper extends a 2023 survey by introducing new biology-related questions to evaluate the reasoning capabilities and accessibility of top large language models, aiming to quantify improvements in reasoning and their impact on average users.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes