Related papers: Contrastive Learning Reduces Hallucination in Conversations

Contrastive Learning Reduces Hallucination in Conversations

URL: http://arxiv.org/abs/2212.10400v1
Date: Tue, 20 Dec 2022 16:26:18 GMT
Title: Contrastive Learning Reduces Hallucination in Conversations
Authors: Weiwei Sun, Zhengliang Shi, Shen Gao, Pengjie Ren, Maarten de Rijke, Zhaochun Ren
Abstract summary: We propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches.
Score: 76.55116206021346
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.

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