Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
This addresses the problem of inefficient and unreliable reasoning in LLMs for AI applications, offering a novel approach that is incremental in its method.
The paper tackles the limitations of large language models (LLMs) like hallucination and unfaithful reasoning by introducing LeCo, an intrinsic self-correct reasoning framework that learns from correct steps without human feedback or prompts, improving reasoning performance with reduced token consumption across various tasks.
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content. One potential approach to mitigate these issues is learning from human or external feedback (e.g. tools). In this paper, we introduce an intrinsic self-correct reasoning framework for LLMs that eliminates the need for human feedback, external tools, and handcraft prompts. The proposed framework, based on a multi-step reasoning paradigm \textbf{Le}arning from \textbf{Co}rrectness (\textsc{LeCo}), improves reasoning performance without needing to learn from errors. This paradigm prioritizes learning from correct reasoning steps, and a unique method to measure confidence for each reasoning step based on generation logits. Experimental results across various multi-step reasoning tasks demonstrate the effectiveness of the framework in improving reasoning performance with reduced token consumption.