Summary of Enhancing Temporal Understanding in Llms For Semi-structured Tables, by Irwin Deng et al.
Enhancing Temporal Understanding in LLMs for Semi-structured Tables
by Irwin Deng, Kushagra Dixit, Vivek Gupta, Dan Roth
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); 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 comprehensive analysis is conducted on temporal datasets to identify the limitations of large language models (LLMs) in handling tabular temporal question answering. Enhancements are made to TempTabQA, a dataset designed for this task, and critical insights are provided to improve LLM performance. A novel approach, C.L.E.A.R., is introduced to strengthen LLM capabilities in temporal reasoning. Experimental results demonstrate that the method improves evidence-based reasoning across various models, while indirect supervision with auxiliary data boosts model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have trouble understanding tables and timelines. Researchers studied many datasets to figure out why this is a problem. They improved a special dataset called TempTabQA, which helps LLMs answer questions about tables that change over time. The study shows how to make LLMs better at this task. It also introduces a new way to help LLMs understand timelines and tables. |
Keywords
* Artificial intelligence * Question answering
