Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation

Yixin Wan, Fanyou Wu, Weijie Xu, Srinivasan H. Sengamedu

Keywords: hallucination, knowledge-grounded dialogue generation, natural language generation

Abstract: 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.

Submission Number: 111