RHO : Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023)

Abstract: Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, which further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO (ρ) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG (Moon et al., 2019) show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA (Durmus et al., 2020)).

## introduction an open-domain dialogue system aims to automatically interact with humans with sensible and informative responses. to produce such responses, knowledge-grounded dialogue (kgd) systems are established, which leverage external knowledge such as knowledge graphs (kgs) @xcite . despite impressive progress in general linguistic performance, kgd systems are still prone to the hallucination problem in which the generated response is nonsensical or unfaithful to dialogue history or external knowledge (ji figure 1 : an example of hallucinated kgd. based on the dialogue history and the kg, the system is expected to generate a response. in the response, "patrick stewart" contradicts the @xmath0, directed_by, bryan singer>, which is a case of intrinsic hallucination (in yellow); "in 2004" cannot be verified based on the given information, which is a case of extrinsic hallucination (in pink). @xcite @xcite . two types of hallucinations may exist: intrinsic hallucination (the generated response is contradicted by the dialogue history or the external knowledge) and extrinsic hallucination (the generated response is hard to be verified with the dialogue history and external knowledge) @xcite . as the example in figure 1 , this issue undermines dialogue systems' performance or raises safety concerns in real-world applications. for instance, the recently emerged foundation model chatgpt suffers from this hallucination problem (openai, 2023), especially extrinsic hallucinations. openai currently uses huge amounts of human feedback to fix many chatgpt errors, which is labor-intensive. it would be beneficial to reduce such errors automatically in advance. the heterogeneity between external knowledge and textual dialogue content makes it challenging for neural response generation models to learn the representation and correlation in the input source @xcite @xcite . this challenge could further result in a hallucinated generation that deviates from the source. previous studies have shown that the hallucination problem in kgd can be mitigated by retrieved knowledge augmentation @xcite , control code @xcite , and response postprocessing @xcite . however, these works do not emphasize handling the discrepancy between lexical dialogue and structured knowledge information for the harmony of their fusion. the interaction mechanism between external knowledge and dialogue context should also be clarified. in order to address this issue and take full advantage of lexical and knowledge information, we present rho (ρ) 2 for faithful open-domain dialogue response generation with enhanced knowledge grounding and re-ranking. a high-level framework is illustrated in figure 2 . specifically, rho first learns the structured embeddings of all entities and relation predicates from the kg and links their mentions in the dialogue context to the kg. in the encoder-decoder model, the representations of all the linked entities and relations are grounded by kg embeddings, both locally and globally. here, local knowledge grounding refers to the process where an entity or relation predicate receives and fuses its kg embedding only. while in global knowledge grounding, each entity or relation predicate attentively learns the knowledge from the entire sub-graph stored in a memory bank @xcite , which assigns dynamic weights of each triple equipping rho with multi-hop reasoning abilities. these two knowledge groundings help the model effectively encode and inject the knowledge information from context-related subgraph with proper attention. in addition, we re-rank the generated responses according to the hallucination degree. this technique utilizes conversational reasoning to enforce the whole conversation to follow the knowledge traverses throughout kg. in the experiments, we show that rho outperforms state-of-the-art (sota) @xcite on the opendialkg @xcite dataset by a large margin: improving 17.54% in feqa @xcite , and reducing 32.93% hallucinations according to human evaluation. in particular, the responses have a broader coverage of entities and relations in the kg, demonstrating higher faithfulness of responses. the quantitative and qualitative analysis further shows its effectiveness in reducing hallucination while not sacrificing conversational abilities. in summary, the major contribu- ## methodology in this section, we begin with a brief introduction to our kgd task. then, the detailed techniques of rho are presented. please refer to figure 2 for an overview of our approach. rho incorporates both textual and structured information from external kg into dialogue system via knowledgeable task guidance ( § 3.2) and enhanced knowledge groundings ( § 3.3, § 3.4). specifically, we introduce local token-level knowledge grounding in § 3.3 and global grounding to provide a comprehensive view and the multi-hop reasoning ability in § 3.4. to effectively encode the heterogeneous sources, we embeddig layer n×transformer layer multi-head attention local knowledge grounding triple 2 superman returns bryan singer directed by x-men 2 directed by triple 1 sub-graph given the knowledge ? x -men 2 ... ... input ... textual embedding context-knowledge cross attention triple 1 ... ... triple 2 projection layer global knowledge grounding encoder figure 3: the diagram of the encoder with proposed local and global knowledge groundings. ## conclusion and future work in this work, we investigate the hallucination in kgd systems and propose the rho framework to tackle this issue with three mechanisms: local knowledge grounding, global knowledge grounding, and response re-ranking. our method is empirically proven effective in reducing hallucinations with automatic and human evaluation. we also conduct deep error analysis on the generated responses. for future work, the re-ranking module can be combined with foundation models such as chatgpt to reduce manual labor in the human feedback process. we also call for research to strike a better balance between response faithfulness and diversity.