Towards Fewer Hallucinations in Knowledge-Grounded Dialogue Generation via Augmentative and Contrastive Knowledge-Dialogue (2023)

Abstract: Existing knowledge-grounded open-domain dialogue generation models often face the hallucination problem, i.e. the dialogue generative model will persist in an inappropriate knowledge and generate responses that inconsistent with the facts. We argue that this problem mainly stems from the polarized optimization objectives and weak knowledge generation ability. To mitigate the hallucination, we take inspiration from human communicating that people will replay euphemistic responses for the unclear or unrecognizable knowledge, and propose an Augmentative and Contrastive Knowledge Dialogue Expansion Framework (ACK-DEF). ACK-DEF constructs the augmentative and contrastive knowledge dialogue samples, which consist of the knowledge of different degrees of errors and the response of manual design, to expand the original training set and smooth the polarized optimization objective that enables models to generate ground-truth with or without gold knowledge. Not only the knowledge, ACK-DEF also provides the tactful responses of manual design corresponding to the incomplete correct knowledge. Experimental results on the Wikipedia of Wizard dataset show that employing the ACK-DEF is effective to alleviate the hallucination problem.

## introduction recently, knowledge-grounded dialogue generation draws dramatic attentions in artificial intelligence community. many efforts incorporate knowledge information to improve the performance of dialogue generation models @xcite @xcite @xcite @xcite . however, these methods always face the hallucination problem, that is, the dialogue generation model may insist on an inappropriate knowledge and generate responses that inconsistent with the facts @xcite @xcite . we argue that the hallucination problem primarily caused by two aspects: (1) the optimization objective is usually polarized by the gold knowledgedialogue samples and general dialogue samples without knowledge in current knowledge-grounded dialogue datasets @xcite @xcite @xcite . few datasets consider teaching models how to respond when dealing with incomplete correct knowledge, which makes the models tend to believe in the given knowledge, regardless of whether the knowledge is appropriate or not, resulting in hallucination problems. in addition, the knowledge retrieval system tends to extract irrelevant knowledge rather than relevant knowledge when the database is large, aggravating the hallucinations @xcite . (2) the generation of knowledge may also face the hallucination problem and obtain the inappropriate knowledge, leading the generation of hallucination responses @xcite @xcite @xcite . to mitigate the hallucination problem, we propose an augmentative and contrastive knowledge dialogue expansion framework (ack-def), which is inspirited by human communicating that people will replay euphemistic response for the unrecognizable knowledge. ack-def is proposed to smooth the polarized optimization objective by augmenting training set with augmentative and contrastive knowledge-dialogue samples. not only the knowledge, we also designed the reply patterns for the knowledge with different level of errors. for this, we propose the augmentative knowledge dialogue expansion (ak), and contrastive knowledge dialogue expansion (ck). ak is proposed to boost the generalization ability of models on knowledge with minor noise. on the contrary, inspired from the contrastive learning paradigm @xcite @xcite , ck reconstructs incorrect knowledge and designs euphemistic responses, which aims to push the model learn the reply pattern of incorrect knowledge and a better boundary between correct and incorrect knowledge. contributions: we propose an ack-def to construct new knowledge-dialogue samples that consist of knowledge with different level of errors and manual responses, to soften the training optimization objectives of models, which will mitigate the hallucination. finally, we conduct extension experiments to show the superior performance of ack-def on alleviating the hallucination. ## methodology to mitigate the hallucination problem that caused by the polarized optimization objectives in knowledge grounded dialogue generation, we take inspiration from human communicating, and propose the augmentative and contrastive knowledge dialogue expansion framework (ack-def). our ack-def aims to soften the polarized training optimization objectives of current knowledgegrounded dialogue generation methods, and guide the dialogue system reply patterns for the knowledge with different level of errors. to achieve this end, we design two effective expansion method, which will be detailed in below. ## conclusion this paper focuses on the hallucinations caused by polarized optimization objective in knowledgegrounded dialogue generation (kgdg), and proposes an augmentative and contrastive knowledge dialogue expansion framework (ack-def) to mitigate it. the optimization objective of kgdg is to train the model could generate proper response with or without knowledge, which inevitably weaken the model's ability on unrecognized knowledge and lead hallucinations. therefore, ack-def constructs multiple level knowledge-dialogue samples to soften the optimization objective of kgdg. extension experimental results show the superior performance of using our methods on dialogue metrics and knowledge correlations.