A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation

• URL: http://arxiv.org/abs/2404.03491v1
• Date: Thu, 4 Apr 2024 14:45:26 GMT
• Title: A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
• Authors: Jifan Yu, Xiaohan Zhang, Yifan Xu, Xuanyu Lei, Zijun Yao, Jing Zhang, Lei Hou, Juanzi Li,
• Abstract summary: We propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance.
• Score: 51.53917938874146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the hallucination problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good performance on this issue. As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this paper, we analyze the causal story behind this problem with counterfactual reasoning methods. Based on the causal effect analysis, we propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance, while keeping adaptive to different generation models. We hope our efforts can support and call for more attention to developing lightweight techniques towards robust and trusty dialogue systems.

Related papers

• Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
  We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback. We generate a small-size hallucination annotation dataset by proprietary models. Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
  arXiv  Detail & Related papers  (2024-04-22T14:46:10Z)
• Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models [11.492702369437785]
  Hallucinations are semantically unrelated to the source utterance, yet still fluent and coherent. We show that commonly used metrics, such as word error rates, cannot differentiate between hallucinatory and non-hallucinatory models. We devise a framework for identifying hallucinations by analysing their semantic connection with the ground truth and their fluency.
  arXiv  Detail & Related papers  (2024-01-03T06:56:56Z)
• HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
  hallucinations inherent in machine-generated data remain under-explored. We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
  arXiv  Detail & Related papers  (2023-11-22T04:52:58Z)
• Towards Mitigating Hallucination in Large Language Models via Self-Reflection [63.2543947174318]
  Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets.
  arXiv  Detail & Related papers  (2023-10-10T03:05:44Z)
• Diving Deep into Modes of Fact Hallucinations in Dialogue Systems [2.8360662552057323]
  Knowledge Graph(KG) grounded conversations often use large pre-trained models and usually suffer from fact hallucination. We build an entity-level hallucination detection system, which would provide fine-grained signals that control fallacious content while generating responses.
  arXiv  Detail & Related papers  (2023-01-11T13:08:57Z)
• Contrastive Learning Reduces Hallucination in Conversations [76.55116206021346]
  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.
  arXiv  Detail & Related papers  (2022-12-20T16:26:18Z)
• Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding [15.62141731259161]
  We focus on the task of improving the faithfulness of Neural Dialogue Systems to known facts supplied by a Knowledge Graph (KG) We propose Neural Path Hunter which follows a generate-then-refine strategy whereby a generated response is amended using the k-hop subgraph of a KG. Our proposed model can easily be applied to any dialogue generated responses without retraining the model.
  arXiv  Detail & Related papers  (2021-04-17T05:23:44Z)
• Retrieval Augmentation Reduces Hallucination in Conversation [49.35235945543833]
  We explore the use of neural-retrieval-in-the-loop architectures for knowledge-grounded dialogue. We show that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks.
  arXiv  Detail & Related papers  (2021-04-15T16:24:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.