Summary of Llms Are Not Zero-shot Reasoners For Biomedical Information Extraction, by Aishik Nagar et al.
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
by Aishik Nagar, Viktor Schlegel, Thanh-Tung Nguyen, Hao Li, Yuping Wu, Kuluhan Binici, Stefan Winkler
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper explores the capabilities of Large Language Models (LLMs) on biomedical tasks, such as Medical Classification and Named Entity Recognition (NER). Despite their success on other tasks, it is unclear how well LLMs perform on these tasks. The authors systematically benchmark the performance of various open LLMs, including BioMistral and Llama-2 models, on a diverse set of biomedical datasets. They evaluate different prompting methods, including standard prompting, Chain-of-Thought (CoT), Self-Consistency, and Retrieval-Augmented Generation (RAG) with PubMed and Wikipedia corpora. The results show that standard prompting consistently outperforms more complex techniques across both tasks, highlighting the limitations of current advanced prompting methods in the biomedical domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper compares how well Large Language Models do on medical tasks like classifying information and identifying important words. While they’re good at other things, it’s not clear if they’re good at these tasks too. The authors test different ways of “asking” the models questions to see what works best. They find that just using simple language gets the best results, which is surprising because more complicated methods were expected to do better. |
Keywords
» Artificial intelligence » Classification » Llama » Named entity recognition » Ner » Prompting » Rag » Retrieval augmented generation
