Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation
This addresses the issue of factually incorrect outputs in LLMs for users needing reliable text generation, though it is an incremental improvement over existing mitigation techniques.
The paper tackles the problem of hallucinations in large language models by introducing Monitoring Decoding, a framework that monitors and intervenes during generation to revise hallucination-prone tokens, achieving higher factual accuracy and reduced computational overhead compared to self-consistency methods.
While large language models have demonstrated exceptional performance across a wide range of tasks, they remain susceptible to hallucinations -- generating plausible yet factually incorrect contents. Existing methods to mitigating such risk often rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucinated outputs with high confidence. To address these limitations, we introduce Monitoring Decoding (MD), a novel framework that dynamically monitors the generation process and selectively applies in-process interventions, focusing on revising crucial tokens responsible for hallucinations. Instead of waiting until completion of multiple full-length generations, we identify hallucination-prone tokens during generation using a monitor function, and further refine these tokens through a tree-based decoding strategy. This approach ensures an enhanced factual accuracy and coherence in the generated output while maintaining efficiency. Experimental results demonstrate that MD consistently outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead.