Summary of Harnessing Multi-role Capabilities Of Large Language Models For Open-domain Question Answering, by Hongda Sun et al.
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
by Hongda Sun, Yuxuan Liu, Chengwei Wu, Haiyu Yan, Cheng Tai, Xin Gao, Shuo Shang, Rui Yan
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 proposed LLMQA framework addresses the limitations of existing open-domain question answering (ODQA) methods by combining the strengths of retrieval-based and generation-based evidence collection. The framework consists of three steps: query expansion, document selection, and answer generation. Large language models (LLMs) play multiple roles within this process, serving as generators, rerankers, and evaluators to collaborate in producing high-quality answers. A novel prompt optimization algorithm is introduced to refine role-playing prompts and improve the quality of evidence and answers. Experimental results on widely used benchmarks demonstrate that LLMQA achieves the best performance in terms of both answer accuracy and evidence quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Open-domain question answering (ODQA) is a new way for computers to understand questions and find answers from a wide range of sources. Right now, there are two main ways to do this: either look up relevant documents from an external source or use special language models to create the answer. However, these methods have limitations. To solve this problem, researchers proposed a new framework called LLMQA that combines the best of both worlds. This framework has three steps: expanding the question, selecting relevant documents, and generating the answer. Large language models are used in multiple roles within this process, helping to create better answers. The researchers also developed an algorithm to optimize the prompts for these models, making sure they produce high-quality answers. The results of experiments on well-known benchmarks show that LLMQA works best in terms of both accuracy and quality. |
Keywords
» Artificial intelligence » Optimization » Prompt » Question answering
