QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction
It addresses hallucinations in LLM reasoning for semantic parsing, offering an incremental improvement with specific gains in accuracy and efficiency.
The paper tackles the problem of reliability and efficiency in LLM-based semantic parsing by introducing QueryAgent with ERASER, a self-correction method using environmental feedback, achieving improvements of 7.0 and 15.0 F1 on GrailQA and GraphQ benchmarks.
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs step-wise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 7.0 and 15.0 F1. Moreover, our approach exhibits superiority in terms of efficiency, including runtime, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, revealing the strong transferability of our approach.