Summary of Accurate and Regret-aware Numerical Problem Solver For Tabular Question Answering, by Yuxiang Wang et al.
Accurate and Regret-aware Numerical Problem Solver for Tabular Question Answering
by Yuxiang Wang, Jianzhong Qi, Junhao Gan
First submitted to arxiv on: 10 Oct 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 presents a novel approach to question answering on free-form tables, known as TableQA. The authors build upon recent studies that utilize Large Language Models (LLMs) for this task, but aim to address the limitations of these models when dealing with numerical values in tabular data. They propose a model called TabLaP, which uses LLMs as a planner rather than an answer generator, leveraging their multi-step reasoning capabilities while delegating numerical calculations to a Python interpreter. The authors also attempt to quantify the trustworthiness of TabLaP’s answers, allowing users to make regret-aware decisions. Experimental results on two benchmark datasets demonstrate that TabLaP outperforms state-of-the-art models by 5.7% and 5.8%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TableQA is a challenging task because tables have flexible structures and complex schemas. Researchers use Large Language Models (LLMs) to help with this task, but LLMs struggle with numerical values in tables. This paper proposes a new model called TabLaP that uses LLMs as a planner, not just an answer generator. It’s like having a conversation with the table, then doing some math to get the right answer. The authors also try to figure out how trustworthy the answers are, so users can make good decisions. They tested their model on two datasets and it was way better than other models. |
Keywords
» Artificial intelligence » Question answering
