Summary of Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension, by Jio Oh et al.
Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension
by Jio Oh, Geon Heo, Seungjun Oh, Jindong Wang, Xing Xie, Steven Euijong Whang
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed technique, Thinking with Tables, helps Large Language Models (LLMs) process complex queries more effectively by leveraging tables for intermediate thinking. By introducing a pre-instruction that prompts LLMs to organize information in tables, the approach achieves an average 40.29% relative performance increase and improved robustness across different requests, conditions, or scenarios. The technique also demonstrates generalizability and can be applied to various real-world scenarios. To evaluate its effectiveness, four distinct structuring levels are introduced, showcasing the influence of data structuredness on model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Thinking with Tables is a new way for Large Language Models (LLMs) to understand complex questions that involve multiple conditions. Currently, LLMs struggle with these types of questions. The technique helps by asking the LLM to organize information in tables before trying to answer the question. This makes the LLM better at answering the question and also more robust when faced with different scenarios or requests. The approach works well and can be applied to real-world situations. |
