Summary of Hiddentables & Pyqtax: a Cooperative Game and Dataset For Tableqa to Ensure Scale and Data Privacy Across a Myriad Of Taxonomies, by William Watson et al.
HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies
by William Watson, Nicole Cho, Tucker Balch, Manuela Veloso
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A recently proposed Large Language Model (LLM) faces challenges in analyzing table question-answering tasks due to limitations in context windows, tokenization patterns, and data confidentiality. To address this issue, the authors introduce “HiddenTables”, a cooperative game between an LLM code-generator (“Solver”) and an Oracle that evaluates the model’s ability to solve Table QA tasks using natural language schemas while ensuring data security. The authors demonstrate the collective inability of LLMs to generalize complex queries, handle compositional dependencies, and align natural language to programmatic commands when provided with concrete table schemas. Notably, “HiddenTables” improves efficiency in prompt and completion tokens compared to encoder-based models. Additionally, a new dataset called “PyQTax” is proposed, comprising 116,671 question-table-answer triplets with fine-grained breakdowns and labels for varying question taxonomies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how some language models struggle when trying to answer questions from big tables. The authors found that these models have trouble because they can only look at a small part of the table at a time, and it’s hard to make sense of all the information. To fix this problem, they created a game called “HiddenTables” where two models work together to come up with good answers. They tested their approach and found that it actually works better than other methods. The authors also released a new dataset that includes many questions and table answers. |
Keywords
» Artificial intelligence » Encoder » Large language model » Prompt » Question answering » Tokenization
