The ART of LLM Refinement: Ask, Refine, and Trust
This addresses the challenge of improving LLM reliability in reasoning for applications like math and QA, though it is incremental as it builds on self-refinement concepts.
The paper tackles the problem that LLMs struggle to accurately self-refine their outputs in reasoning tasks, proposing ART (Ask, Refine, and Trust) to decide when to refine and trust refinements, achieving a +5 point performance gain over baselines on GSM8K and StrategyQA.
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referred to as self-refinement, postulates that LLMs can detect and correct the errors in their generations when asked to do so. However, recent empirical evidence points in the opposite direction, suggesting that LLMs often struggle to accurately identify errors when reasoning is involved. To address this, we propose a reasoning with refinement objective called ART: Ask, Refine, and Trust, which asks necessary questions to decide when an LLM should refine its output, and either affirm or withhold trust in its refinement by ranking the refinement and the initial prediction. On two multistep reasoning tasks of mathematical word problems (GSM8K) and question answering (StrategyQA), ART achieves a performance gain of +5 points over self-refinement baselines, while using a much smaller model as the decision maker. We also demonstrate the benefit of using smaller models to make refinement decisions as a cost-effective alternative to fine-tuning a larger model.