Summary of Interpretable Llm-based Table Question Answering, by Giang (dexter) Nguyen et al.
Interpretable LLM-based Table Question Answering
by Giang, Nguyen, Ivan Brugere, Shubham Sharma, Sanjay Kariyappa, Anh Totti Nguyen, Freddy Lecue
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 approach to Table Question Answering (Table QA) is introduced, addressing the critical need for interpretability in high-stakes industries like finance and healthcare. The proposed Plan-of-SQLs (POS) method uses SQL executions to answer input queries efficiently and effectively, providing explanations that are preferred by both human judges and Large Language Models (LLMs). POS helps users understand model decision boundaries, facilitates error identification, and achieves competitive or superior accuracy in standard benchmarks while requiring fewer LLM calls and database queries. This approach has the potential to significantly improve Table QA performance and provide valuable insights into model decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method for answering questions about tables has been developed. It’s called Plan-of-SQLs (POS). The goal of POS is to help people understand how it arrived at its answers, which is important in industries like finance and healthcare where decisions have big consequences. The approach uses SQL code to find the answer, which makes it more efficient than other methods that use Large Language Models (LLMs). When tested with human judges and LLMs, POS was preferred because it provides clear explanations of how it arrived at its answers. This could help people understand why a model is making certain decisions or where it might be going wrong. |
Keywords
» Artificial intelligence » Question answering
