Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination
arXiv:2602.15509v1 Announce Type: new Abstract: The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses that may mislead users and undermine system trust. Existing refinement methods for dialogue systems typically operate at the response level, overlooking the fact that a single response may contain multiple verifiable or unverifiable facts. To address this gap, we propose Fine-Refine, a fine-grained refinement framework that decomposes responses into atomic units, verifies each unit using external knowledge, assesses fluency via perplexity, and iteratively corrects granular errors. We evaluate factuality across the HybriDialogue and OpendialKG datasets in terms of factual accuracy (fact score) and coverage (Not Enough Information Proportion), and experiments show that Fine-Refine substantially improves factuality, achieving up to a 7.63-point gain in dialogue fact score, with
arXiv:2602.15509v1 Announce Type: new Abstract: The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses that may mislead users and undermine system trust. Existing refinement methods for dialogue systems typically operate at the response level, overlooking the fact that a single response may contain multiple verifiable or unverifiable facts. To address this gap, we propose Fine-Refine, a fine-grained refinement framework that decomposes responses into atomic units, verifies each unit using external knowledge, assesses fluency via perplexity, and iteratively corrects granular errors. We evaluate factuality across the HybriDialogue and OpendialKG datasets in terms of factual accuracy (fact score) and coverage (Not Enough Information Proportion), and experiments show that Fine-Refine substantially improves factuality, achieving up to a 7.63-point gain in dialogue fact score, with a small trade-off in dialogue quality.
Executive Summary
Fine-Refine, a fine-grained refinement framework, is proposed to mitigate dialogue hallucination in large language models. By decomposing responses into atomic units, verifying each unit, and assessing fluency, Fine-Refine iteratively corrects granular errors. Experiments on HybriDialogue and OpendialKG datasets show a substantial improvement in factuality, with a 7.63-point gain in dialogue fact score. However, there is a small trade-off in dialogue quality. While Fine-Refine addresses a significant gap in existing refinement methods, its effectiveness in real-world applications remains to be seen.
Key Points
- ▸ Fine-Refine decomposes responses into atomic units to improve factuality
- ▸ The framework verifies each unit using external knowledge and assesses fluency via perplexity
- ▸ Experiments demonstrate a significant improvement in factuality, but with a small trade-off in dialogue quality
Merits
Strength in addressing a significant gap in refinement methods
Fine-Refine effectively tackles the issue of hallucination in dialogue systems by operating at the response level, unlike existing methods.
Improvement in factuality
The framework achieves a substantial gain in dialogue fact score, demonstrating its effectiveness in mitigating hallucination.
Flexibility in application
Fine-Refine can be adapted to various dialogue systems and tasks, making it a valuable tool for developers.
Demerits
Small trade-off in dialogue quality
The improvement in factuality comes at the cost of a small decrease in dialogue quality, which may be a concern for some applications.
Limited evaluation on real-world datasets
While experiments demonstrate the effectiveness of Fine-Refine, it is essential to evaluate its performance on real-world datasets to ensure its practical applicability.
Lack of consideration for human evaluation
The framework focuses on quantitative metrics, neglecting the importance of human evaluation in assessing the quality and accuracy of generated responses.
Expert Commentary
Fine-Refine is a well-designed and effective framework for mitigating dialogue hallucination. However, its limitations and trade-offs should be carefully considered in its implementation and deployment. As the field continues to evolve, it is essential to evaluate Fine-Refine's performance on real-world datasets and consider its integration with other refinement methods and techniques. Furthermore, the framework's potential impact on dialogue quality and human evaluation should be thoroughly examined to ensure its practical applicability.
Recommendations
- ✓ Developers should integrate Fine-Refine into their dialogue systems to improve factuality and mitigate hallucination.
- ✓ Researchers should evaluate Fine-Refine's performance on real-world datasets and consider its integration with other refinement methods and techniques.
- ✓ Policy makers should consider the potential implications of Fine-Refine on dialogue quality and human evaluation, and develop guidelines for its deployment and use.
