Summary of Call Me When Necessary: Llms Can Efficiently and Faithfully Reason Over Structured Environments, by Sitao Cheng et al.
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
by Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
First submitted to arxiv on: 13 Mar 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 introduces Reasoning-Path-Editing (Readi), a novel framework for large language models (LLMs) to efficiently and faithfully reason over structured environments, such as knowledge graphs and tables. Unlike previous methods that incrementally build a reasoning path through step-by-step interactions with the environment, Readi allows LLMs to initially generate a reasoning path and edit it only when necessary. The proposed framework is evaluated on three KGQA and two TableQA datasets, showing significant improvements over previous LLM-based methods and comparable performance with state-of-the-art fine-tuned methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can better understand and reason with structured data like knowledge graphs and tables. The authors propose a new way for computers to generate answers from this type of data, called Reasoning-Path-Editing (Readi). Unlike previous approaches that slowly build an answer path, Readi lets the computer quickly come up with an answer and then make small adjustments as needed. This method is tested on several datasets and shows significant improvements over other methods. |
