Thinking, Faithful and Stable: Mitigating Hallucinations in LLMs

TL;DR

This paper introduces a reinforcement learning framework that uses confidence and entropy signals to detect and reduce hallucinations in LLMs, enhancing reasoning stability and faithfulness.

Contribution

It presents a novel self-correcting RL approach that leverages fine-grained uncertainty signals to improve LLM reasoning and reduce hallucinations.

Findings

Improves final answer accuracy in LLMs.

Enhances reasoning calibration and faithfulness.

Validates individual contribution of uncertainty signals.

Abstract

This project develops a self correcting framework for large language models (LLMs) that detects and mitigates hallucinations during multi-step reasoning. Rather than relying solely on final answer correctness, our approach leverages fine grained uncertainty signals: 1) self-assessed confidence alignment, and 2) token-level entropy spikes to detect unreliable and unfaithful reasoning in real time. We design a composite reward function that penalizes unjustified high confidence and entropy spikes, while encouraging stable and accurate reasoning trajectories. These signals guide a reinforcement learning (RL) policy that makes the model more introspective and shapes the model's generation behavior through confidence-aware reward feedback, improving not just outcome correctness but the coherence and faithfulness of their intermediate reasoning steps. Experiments show that our method improves both final answer accuracy and reasoning calibration, with ablations validating the individual contribution of each signal.