Summary of Beamaggr: Beam Aggregation Reasoning Over Multi-source Knowledge For Multi-hop Question Answering, by Zheng Chu et al.
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
by Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
First submitted to arxiv on: 28 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 This paper proposes a new framework for knowledge-intensive multi-hop question answering called Beam Aggregation Reasoning (BeamAggR). Large language models (LLMs) have shown strong reasoning capabilities but still struggle with factual errors. BeamAggR addresses this issue by exploring and prioritizing promising answers at each hop of the question, using a combination of bottom-up reasoning and probabilistic aggregation. The framework is designed to handle complex questions by parsing them into trees, then using multi-source knowledge to retrieve answer candidates for atomic questions and combining beam candidates for composite questions. Experimental results on four open-domain datasets show that BeamAggR outperforms state-of-the-art methods by 8.5%. Additionally, the analysis reveals better knowledge collaboration and answer aggregation with BeamAggR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers answer questions when they need to use information from multiple sources. Right now, these computers can get some answers wrong even if they have lots of knowledge. The new method is called Beam Aggregation Reasoning and it helps the computer find the best answer by considering different possibilities and combining the most promising ones. It works by breaking down complex questions into smaller parts and using information from multiple sources to find the right answers. In tests, this method did much better than previous methods at answering these kinds of questions. |
Keywords
» Artificial intelligence » Parsing » Question answering
