Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
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Hannah Rashkin, David Reitter, Gaurav Singh Tomar, Dipanjan Das. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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It is acceptable for open-domain dialogue systems to respond in chit-chat style, subjective style, or with a relevant fact not necessarily present in the user input, history or provided knowledge source, which improves engagingness and diversity.
“For instance, it is acceptable for open-domain dialogue systems to respond in chit-chat style, subjective style [214], or with a relevant fact that is not necessarily present in the user input, history or provided knowledge source - this improves the engagingness and diversity of the dialogue generation.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [214]
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Experimental — accuracy not yet measuredRashkin et al. (2021) showed that faithfulness in knowledge-grounded dialogues can be improved by using controllable features based on NLI model predictions.
“Also relevant to our work, Rashkin et al. (2021) showed that faithfulness in knowledgegrounded dialogues can be improved by using controllable features based on NLI model predictions.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Rashkin et al. (2021)
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Ma, L., Li, J., Li, M., Zhang, W., & Liu, T. (2023) · ACM Transactions on Information Systems · doi:10.1145/3617829 · Cited by 2 · see citing works
Read Then Respond: Multi-granularity Grounding Prediction for Knowledge-Grounded Dialogue Generation
Du, Y., Zhang, S., Wu, X., Yan, Z., Cao, Y., & Li, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46664-9_20 · Cited by 2 · see citing works
Enhancing Multilingual Document-Grounded Dialogue Using Cascaded Prompt-Based Post-Training Models
Liu, J., Cheng, S., Zhou, Z., Gu, Y., Ye, J., & Luo, H. (2023) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2023.dialdoc-1.5 · Cited by 2 · see citing works
Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable?
He, M., Jia, T., Duan, C., Xiao, P., Zhang, L., Wang, K., Wu, Y., Li, Y., & Huang, G. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00316 · Cited by 2 · see citing works
Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations
Wu, Z., Balloccu, S., Helaoui, R., Recupero, D. R., & Riboni, D. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.9 · Cited by 2 · see citing works
Priya, P., Tripathi, A. M., Varshney, D., Firdaus, M., & Ekbal, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3548628 · Cited by 1 · see citing works
Lotfi, E., Bruyn, M. D., Buhmann, J., & Daelemans, W. M. P. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.47 · Cited by 1 · see citing works
Evaluating Parameter Efficient Learning for Generation
Xu, P., Patwary, M. A., Prabhumoye, S., Adams, V., Prenger, R., Ping, W., Lee, N., Shoeybi, M., & Catanzaro, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.319 · Cited by 1 · see citing works
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
Daheim, N., Thulke, D., Dugast, C., & Ney, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.98 · Cited by 1 · see citing works
PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems
Wilie, B., Xu, Y., Chung, W., Cahyawijaya, S., Lovenia, H., & Fung, P. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.63 · Cited by 1 · see citing works
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
Daheim, N., Dziri, N., Sachan, M., Gurevych, I., & Ponti, E. M. (2023) · arXiv · doi:10.48550/arxiv.2303.17574
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Infusing Context and Knowledge Awareness in Multi-turn Dialog Understanding
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CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
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Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
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Dialogue Generation with Skeleton Extraction and Causal Intervention
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Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Yang, Y., Huang, H., Liu, Y., & Gao, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.982
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation
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A Multi-Hop Reasoning Knowledge Selection Module for Dialogue Generation
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Semantic context improvisational retrieval-augmented generation for empathic conversational AI
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Persona-centric Metamorphic Relation Guided Robustness Evaluation for Multi-turn Dialogue Modeling
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- Authors
- Rashkin, H., Reitter, D., Tomar, G. S., & Das, D.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 704-718
- OpenAlex
- W3176098057
- Cited by
- 74
- References
- 31
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 10 · 2023: 37 · 2022: 19 · 2021: 2
- FWCI
- 7.85
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- knowledge-grounded dialogue, faithfulness, dialogue systems, knowledge grounding, controllable generation
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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