Summary of Survey on Reasoning Capabilities and Accessibility Of Large Language Models Using Biology-related Questions, by Michael Ackerman
Survey on Reasoning Capabilities and Accessibility of Large Language Models Using Biology-related Questions
by Michael Ackerman
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This research paper explores the intersection of Large Language Models (LLMs) and biomedicine, highlighting advancements made over the past decade. The study integrates Natural Language Processing techniques into biomedical applications, focusing on the top two LLMs. A new survey is conducted to quantify improvements in reasoning abilities and user experiences. The paper also extends research on biological literature retrieval by prompting LLMs to answer open-ended questions in-depth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This study looks at how Large Language Models (LLMs) have become more helpful in the field of medicine over the past 10 years. Researchers combined these models with Natural Language Processing techniques to make medical research easier and more accurate. The study also wants to know if LLMs are getting better at answering complex questions and making smart decisions. Additionally, it explores how well LLMs can find and understand biological information. |
Keywords
» Artificial intelligence » Natural language processing » Prompting
