Summary of Conv-coa: Improving Open-domain Question Answering in Large Language Models Via Conversational Chain-of-action, by Zhenyu Pan et al.
Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action
by Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu
First submitted to arxiv on: 28 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 The paper presents a Conversational Chain-of-Action (Conv-CoA) framework for Open-domain Conversational Question Answering (OCQA). The framework addresses three major challenges: unfaithful hallucination, weak reasoning performance, and unsatisfying information retrieval. Conv-CoA includes a dynamic reasoning-retrieval mechanism that extracts the question intent, decomposes it into a reasoning chain, and solves it via systematic prompting, pre-designed actions, updating the Contextual Knowledge Set (CKS), and a novel Hopfield-based retriever. The framework also proposes a resource-efficiency Hopfield retriever to enhance efficiency and accuracy. Additionally, a conversational-multi-reference faith score (Conv-MRFS) verifies and resolves conflicts between retrieved knowledge and answers. The paper compares Conv-CoA with 23 state-of-the-art methods across five research directions and two public benchmarks, demonstrating that it outperforms others in both accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for computers to answer questions by understanding what you want to know. It’s called Conversational Chain-of-Action (Conv-CoA). This system solves three big problems: making things up, not being good at solving puzzles, and not finding the right information. Conv-CoA works by first figuring out what you’re asking, then breaking it down into smaller steps to solve. It also uses a new way of searching for answers that’s more efficient and accurate. The system is tested against 23 other ways of answering questions and does better in both how well it answers and how fast it answers. |
Keywords
» Artificial intelligence » Hallucination » Prompting » Question answering
