Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
This addresses the problem of unreliable responses in AI dialogue systems for users, though it is incremental as it builds on existing decoding-time mitigation approaches.
The paper tackles hallucination in knowledge-grounded dialogue generation by proposing sequence-level certainty as a theme, showing that higher certainty correlates with lower hallucination, and introduces Certainty-based Response Ranking (CRR) methods that reduce hallucination by up to 15% in experiments across multiple datasets and models.
In this work, we propose sequence-level certainty as a common theme over hallucination in Knowledge Grounded Dialogue Generation (KGDG). We explore the correlation between the level of hallucination in model responses and two types of sequence-level certainty: probabilistic certainty and semantic certainty. Empirical results reveal that higher levels of both types of certainty in model responses are correlated with lower levels of hallucination. We further propose Certainty-based Response Ranking (CRR), a decoding-time hallucination mitigation method that samples several response candidates, ranks them based on sequence-level certainty, and outputs the response with the highest certainty level. Aligning with our definitions of sequence-level certainty, we design 2 types of CRR approaches: Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR). P-CRR ranks individually sampled model responses using the arithmetic mean log-probability of the entire sequence. S-CRR approaches certainty estimation from meaning-space, and ranks model response candidates based on their semantic certainty level as measured by an entailment-based Agreement Score (AS). Through extensive experiments across 3 KGDG datasets, 3 decoding methods, and 4 KGDG models, we validate the effectiveness of CRR for reducing hallucination in KGDG task.