Summary of Debate As Optimization: Adaptive Conformal Prediction and Diverse Retrieval For Event Extraction, by Sijia Wang and Lifu Huang
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction
by Sijia Wang, Lifu Huang
First submitted to arxiv on: 18 Jun 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 A novel multi-agent debate system, dubbed DAO (Debate as Optimization), is proposed to iteratively refine large language models’ outputs without requiring parameter tuning. The system consists of two modules: DRAG (Diverse-RAG) and AdaCP (Adaptive Conformal Prediction). DRAG retrieves supporting information for the debate discussion, while AdaCP enhances accuracy and reliability by rejecting less promising answers. Experimental results show a significant reduction in the performance gap between supervised approaches and tuning-free LLM-based methods on event detection and argument extraction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This system helps large language models give better answers without needing to be trained on specific data. It’s like having a debate team refine their arguments and then uses that refined information to make more accurate predictions. |
Keywords
» Artificial intelligence » Event detection » Optimization » Rag » Supervised
