Summary of Automating Knowledge Discovery From Scientific Literature Via Llms: a Dual-agent Approach with Progressive Ontology Prompting, by Yuting Hu et al.
Automating Knowledge Discovery from Scientific Literature via LLMs: A Dual-Agent Approach with Progressive Ontology Prompting
by Yuting Hu, Dancheng Liu, Qingyun Wang, Charles Yu, Heng Ji, Jinjun Xiong
First submitted to arxiv on: 20 Aug 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 The paper introduces LLM-Duo, a novel framework for automating knowledge discovery from scientific literature. It combines large language models (LLMs) with a progressive ontology prompting algorithm and a dual-agent system. The POP algorithm guides LLMs to discover knowledge through structured prompt templates and action orders. Two specialized LLM agents, an explorer and an evaluator, work together to enhance reliability. Experiments show the method outperforms baselines, enabling accurate annotations. A case study in speech-language therapy discovery identifies 2,421 interventions from 64,177 articles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper has a new way to help find important information in many scientific papers. It uses special computer programs called large language models (LLMs) and an algorithm that helps them understand what they’re reading. The LLMs work with two other agents to make sure the results are accurate. This method is better than others, and it can even help people who study how we talk. |
Keywords
» Artificial intelligence » Prompt » Prompting
