Summary of Seek and Solve Reasoning For Table Question Answering, by Ruya Jiang and Chun Wang and Weihong Deng
Seek and Solve Reasoning for Table Question Answering
by Ruya Jiang, Chun Wang, Weihong Deng
First submitted to arxiv on: 9 Sep 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 reveals that Large Language Models (LLMs) can be improved for table-based question answering (TQA) tasks by leveraging their reasoning capabilities. The authors propose a Seek-and-Solve pipeline that instructs the LLM to first seek relevant information and then answer questions, integrating these two stages at the reasoning level into a coherent chain of thought. They also distill a single-step TQA-solving prompt from this pipeline using demonstrations with paths to guide the LLM in solving complex tasks under In-Context Learning settings. The results show improved performance and reliability while being efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TQA is a challenging task for LLMs, requiring task simplification before solving. This paper shows that improving TQA by letting LLMs use their reasoning capabilities can be effective. It proposes a new way to ask the LLM questions about tables, making it seek relevant information and then answer questions. The results show that this approach works better than previous methods. |
Keywords
» Artificial intelligence » Prompt » Question answering
