RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
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Dialogue systems can leverage large pretrained 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 stateof-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)). 1
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RHO has been empirically shown to tackle hallucinations in open-domain dialogues.
“RHO [108] is a framework that uses three mechanisms to tackle hallucinations, namely, local knowledge grounding, global knowledge grounding, and response re-ranking to tackle hallucinations in open-domain dialogues, and has been empirically shown to perform this.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [108]
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Bezerra, R., Gala, A. D. C. O. S., Marcondes, F. S., Durães, D. A., & Nováis, P. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536367
Li, Y. Z., Wang, X., Liu, R., Wang, M., & Luo, X. (2026) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1109/iseae69422.2026.11544327
World2Rules: A Neuro-Symbolic Framework for Learning World-Governing Safety Rules for Aviation
Wang, H., Patrikar, J., & Scherer, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-28079-4_23
Combining feedback enhancement with knowledge graphs in large language models
Zhou, Y., Qin, Y., & Lin, C. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00877-2
Re-KGR: Mitigating Factual Hallucinations via Hybrid Graph Reasoning and Semantic Refinement
Li, Y. Z., Wang, X., Liu, R., Wang, M. Y., & Luo, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2494-4_16
Neurosymbolic Large Language Models: A Survey of Symbolic Integration, Reasoning and Explainability
Rani, M., Mishra, B. K., & Thakker, D. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10794-4 · Cited by 1 · see citing works
- Authors
- Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Fung, Pascale
- Venue
- Findings of the Association for Computational Linguistics: ACL 2023
- Published
- 2023
- Pages
- 4504-4522
- OpenAlex
- W4385570284
- Cited by
- 37
- References
- 54
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 11 · 2023: 5
- FWCI
- 6.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- hallucination mitigation, open-domain dialogue systems, knowledge grounding, knowledge graphs, entity and relation embeddings, multi-hop reasoning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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