Summary of Queryagent: a Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-correction, by Xiang Huang et al.
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction
by Xiang Huang, Sitao Cheng, Shanshan Huang, Jiayu Shen, Yong Xu, Chaoyun Zhang, Yuzhong Qu
First submitted to arxiv on: 18 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 A novel framework called QueryAgent has been proposed for semantic parsing using Large Language Models (LLMs). The framework addresses reliability and efficiency issues by solving a question step-by-step and performing step-wise self-correction. A key innovation is an environmental feedback-based self-correction method called ERASER, which selectively corrects errors based on rich intermediate feedback. Experimental results show that QueryAgent outperforms previous few-shot methods on GrailQA and GraphQ by 7.0 and 15.0 F1, respectively, while also exhibiting improved efficiency in terms of runtime, query overhead, and API invocation costs. By applying ERASER to another baseline (AgentBench), the approach achieves an additional 10-point improvement, highlighting its strong transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to use large language models for understanding human questions. They created a system called QueryAgent that takes a question and breaks it down into smaller steps to make sure it gets answered correctly. If the system makes a mistake, it can correct itself using feedback from its environment. The team tested their approach on several datasets and found that it was much better than previous methods at understanding questions, especially when given only one example to work with. This new approach is also faster and more efficient than previous methods. |
Keywords
» Artificial intelligence » Few shot » Semantic parsing » Transferability
