Retrieval Augmentation Reduces Hallucination in Conversation
Open accessAbstract
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et al., 2021).In this work we explore the use of neural-retrieval-in-the-loop architectures -recently shown to be effective in open-domain QA (Lewis et al., 2020b; Izacard and Grave, 2021b) -for knowledge-grounded dialogue, a task that is arguably more challenging as it requires querying based on complex multi-turn dialogue context and generating conversationally coherent responses.We study various types of architectures with multiple components -retrievers, rankers, and encoder-decoders -with the goal of maximizing knowledgeability while retaining conversational ability.We demonstrate that our best models obtain state-of-the-art performance on two knowledge-grounded conversational tasks.The models exhibit open-domain conversational capabilities, generalize effectively to scenarios not within the training data, and, as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots. * Equal ContributionThe following is a conversation with an AI assistant.The assistant is helpful, creative, clever, and very friendly.Human: Hello, who are you?AI: I am an AI created by OpenAI.How can I help you today?Human: Tell me about Kyunghyun
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Long-context strategies and retrieval-augmented generation techniques have improved LLMs' memory in handling long-term tasks.
“Although long-context strategies (Bertsch et al., 2024) and retrieval-augmented generation techniques (Shuster et al., 2021) have improved LLMs' memory in handling long-term tasks, these text-based memory methods face significant limitations in terms of token count, computational cost, and inference time (Zhang et al., 2024).”
From Evaluating the Long-Term Memory of Large Language Models (2025) · cites this paper as Shuster et al., 2021
The Introduction and Discussion (page numbers unavailable in our copy) report that retrieval-augmented models improve performance in knowledge-grounded, multi-turn dialogue. They do not establish that these techniques improve LLMs’ memory on long-term tasks; the paper’s reference to retrieval used “as a memory” appears in Related Work, not as its own finding. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDeep learning based NLG generation is prone to hallucinate unintended text, which degrades system performance and fails to meet user expectations in many real-world scenarios.
“However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [121]
The Introduction says that even large dialogue models generate plausible but factually incorrect statements, and the Conclusion calls this an important problem. The human-evaluation section treats hallucination as an aspect of model performance. Our copy does not establish that it fails user expectations in many real-world scenarios. (LLM, conf=0.82)
Experimental — accuracy not yet measuredHuman evaluation is still one of the most commonly used approaches for evaluating hallucinations in NLG due to the challenging and imperfect nature of current automatic evaluation.
“Due to the challenging and imperfect nature of the current automatic evaluation of hallucinations in NLG, human evaluation [226, 233] is still one of the most commonly used approaches.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [233]
Section 4.2.1 describes human annotation of hallucinations in conversational responses, and Section 5 says standard dialogue metrics are insufficient to capture hallucination. The paper does not establish that human evaluation is one of the most commonly used approaches across NLG. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRAG systems improve factual consistency and provide traceable evidence for generated responses.
“RAG systems improve factual consistency and provide traceable evidence for generated responses (Shuster et al., 2021;Gao et al., 2023).”
From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Shuster et al., 2021
Section 4.2.1 reports that retrieval-augmented models had dramatically lower hallucination rates in human evaluations, supporting improved factuality in the studied conversations. The same section says evaluators were shown a retrieved document to help interpret where a response’s knowledge came from, but it does not establish that RAG systems generally provide traceable evidence for their generated responses. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe efficacy of long-context LLMs and RAG techniques in very long-term dialogues remains unexplored.
“Despite advancements in longcontext large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored.”
From Evaluating Very Long-Term Conversational Memory of LLM Agents (2024) · cites this paper as Shuster et al., 2021
The Introduction (page unavailable) says RAG had not yet been applied to open-domain knowledge-grounded dialogue, supporting a narrower claim about a gap in prior research. The paper then evaluates RAG on multi-turn dialogue; it does not establish that the efficacy of long-context LLMs or RAG in very long-term dialogues remains unexplored. (LLM, conf=0.80)
Experimental — accuracy not yet measured
These methods have shown promising results in significantly reducing hallucinated content and enhancing the accuracy, reliability, and faithfulness of model outputs.
“These methods have shown promising results in significantly reducing hallucinated content and enhancing the accuracy, reliability, and faithfulness of model outputs.”
From Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024) · cites this paper as Shuster et al. 2021
The Introduction reports that the best retrieval-augmented conversational models reduced hallucinated responses by over 60%, and §4.2.1 reports higher knowledgeability. These findings support improved factual accuracy in the tested dialogue tasks, but the paper does not establish improved reliability and faithfulness of model outputs generally. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSuch wrong association results in generation with facts mixed up between two similar entities.
“Such wrong association results in generation with facts mixed up between two similar entities [60, 233].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [233]
Retrieval helps substantially in improving performance on KGD tasks and in reducing hallucination in conversations without sacrificing conversational ability.
“The experimental results show that retrieval helps substantially in improving performance on KGD tasks and in reducing the hallucination in conversations without sacrificing conversational ability.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [233]
Recent language models have a tendency to generate false statements.
“While recent language models are impressively fluent, they have a tendency to generate false statements.”
From TruthfulQA: Measuring How Models Mimic Human Falsehoods (2022) · cites this paper as Shuster et al., 2021
Close inspection of LLM outputs sometimes reveals a range of inaccuracies, from minor errors to flat-out hallucinations.
“Close inspection sometimes reveals a range of inaccuracies, from minor errors to flat-out 'hallucinations' (Shuster et al., 2021) (Figure 1).”
From Inference-Time Intervention: Eliciting Truthful Answers from a Language Model (2023) · cites this paper as Shuster et al., 2021
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Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R., & Nanayakkara, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00530 · Cited by 337 · see citing works
Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024) · Journal of Legal Analysis · doi:10.1093/jla/laae003 · Cited by 288 · see citing works
“Second, precisely because these queries can be answered with tabular data, legal database-augmented LLMs (Cui et al., 2023; Savelka et al., 2023) are likely to soon solve or at least mask hal-lucinated responses to these queries (Shuster et al., 2021; Peng et al., 2023).”
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.05232 · Cited by 2,516 · see citing works
“Shuster et al. (2021) introduced the Knowledge F1 metric to assess how well the model’s generation aligns with the supplied knowledge.”
AI agents under threat: A survey of key security challenges and future pathways
Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., & Xiang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3716628 · Cited by 210 · see citing works
Liu, S., McCoy, A. B., & Wright, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf008 · Cited by 200 · see citing works
Internet-Augmented Dialogue Generation
Komeili, M., Shuster, K., & Weston, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.579 · Cited by 192 · see citing works
Self-citationBeyond Goldfish Memory: Long-Term Open-Domain Conversation
Xu, J., Szlam, A., & Weston, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.356 · Cited by 188 · see citing works
Self-citationTool Learning with Foundation Models
Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.08354 · Cited by 184 · see citing works
Hallucination is Inevitable: An Innate Limitation of Large Language Models
Xu, Z., Jain, S., & Kankanhalli, M. (2024) · arXiv · doi:10.48550/arxiv.2401.11817 · Cited by 180 · see citing works
“Retrieval-based methods reinforce LLM by knowledge graphs or databases to help correct factual errors in models’ outputs [57, 76].”
BackgroundMethodBeyond Goldfish Memory: Long-Term Open-Domain Conversation
Xu, J., Szlam, A., & Weston, J. (2021) · arXiv · doi:10.48550/arxiv.2107.07567 · Cited by 148 · see citing works
Self-citationTowards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141 · see citing works
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Dziri, N., Milton, S., Yu, M., Zaiane, O., & Reddy, S. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.387 · Cited by 121 · see citing works
Learning the natural history of human disease with generative transformers
Shmatko, A., Jung, A. W., Gaurav, K., Brunak, S., Mortensen, L. H., Birney, E., Fitzgerald, T., & Gerstung, M. (2025) · Nature · doi:10.1038/s41586-025-09529-3 · Cited by 110 · see citing works
Pardos, Z. A., & Bhandari, S. (2024) · PLOS ONE · doi:10.1371/journal.pone.0304013 · Cited by 97 · see citing works
Evaluating Very Long-Term Conversational Memory of LLM Agents
Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.747 · Cited by 96 · see citing works
Retrieval-based Language Models and Applications
Asai, A., Min, S., Zhong, Z., & Chen, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts) · doi:10.18653/v1/2023.acl-tutorials.6 · Cited by 81 · see citing works
Yan, L., Martinez-Maldonado, R., Jin, Y., Echeverria, V., Milesi, M., Fan, J., Zhao, L., Alfredo, R., Li, X., & Gašević, D. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105322 · Cited by 85 · see citing works
Contrastive Learning Reduces Hallucination in Conversations
Sun, W., Shi, Z., Gao, S., Ren, P., De Rijke, M., & Ren, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26596 · Cited by 69 · see citing works
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Li, K., Patel, O., Viégas, F., Pfister, H., & Wattenberg, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1797 · Cited by 117 · see citing works
“Close inspection sometimes reveals a range of inaccuracies, from minor errors to flat-out “hallucinations” (Shuster et al., 2021) (Figure 1).”
BackgroundCRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2023) · arXiv · doi:10.48550/arxiv.2305.11738 · Cited by 57 · see citing works
“, 2022), dialogue generation (Shuster et al., 2021), and table-to-text generation (Parikh et al.”
BackgroundDialFact: A Benchmark for Fact-Checking in Dialogue
Gupta, P., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.263 · Cited by 48 · see citing works
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Abootorabi, M. M., Zobeiri, A., Dehghani, M., Mohammadkhani, M., Mohammadi, B., Ghahroodi, O., Baghshah, M. S., & Asgari, E. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.861 · Cited by 76 · see citing works
Language agents achieve superhuman synthesis of scientific knowledge
Skarlinski, M. D., Cox, S., Laurent, J. M., Braza, J. D., Hinks, M., Hammerling, M. J., Ponnapati, M., Rodriques, S. G., & White, A. D. (2024) · arXiv · doi:10.48550/arxiv.2409.13740 · Cited by 49 · see citing works
“To compare the performance of PaperQA2 to other retrieval systems, we evaluated the performance of PaperQA with original parameters, commercial systems like Perplexity 21 and Elicit 22 , and frontier (non-RAG models) on LitQA2.”
MethodInfluentialGastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation
Zhou, Q., Liu, C., Duan, Y., Sun, K., Li, Y., Kan, H., Gu, Z., Shu, J., & Hu, J. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1392555 · Cited by 44 · see citing works
Chain-of-Verification Reduces Hallucination in Large Language Models
Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023) · arXiv · doi:10.48550/arxiv.2309.11495 · Cited by 42 · see citing works
“For example, retrieval-augmented generation can decrease hallucinations by using factual documents for grounding (Shuster et al., 2021; Jiang et al., 2023b; Yu et al., 2023) or chain-of-thought verification (Zhao et al., 2023).”
BackgroundSelf-citationAssisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
Shao, Y., Jiang, Y., Kanell, T., Xu, P., Khattab, O., & Lam, M. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.347 · Cited by 41 · see citing works
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.275 · Cited by 37 · see citing works
Promises and pitfalls of artificial intelligence for legal applications
Kapoor, S., Henderson, P., & Narayanan, A. (2024) · arXiv · doi:10.48550/arxiv.2402.01656 · Cited by 25 · see citing works
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.527 · Cited by 25 · see citing works
Self-citationSanthanam, S., Hedayatnia, B., Gella, S., Padmakumar, A., Kim, S., Liu, Y., & Hakkani-Tur, D. (2021) · arXiv · doi:10.48550/arxiv.2110.05456 · Cited by 21 · see citing works
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity
Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.182 · Cited by 20 · see citing works
Self-citationFActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv · doi:10.48550/arxiv.2305.14251 · Cited by 14 · see citing works
“Most prior work studies factual precision of models supervised for a specific problem such as dialogue (Shuster et al., 2021), or focuses on question answering with short answers (Kadavath et al., 2022; Kandpal et al., 2022; Mallen et al., 2023; Nori et al., 2023).”
BackgroundSun, K., Xu, Y. E., Zha, H., Liu, Y., & Dong, X. L. (2023) · arXiv · doi:10.48550/arxiv.2308.10168 · Cited by 13 · see citing works
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
Shao, Y., Jiang, Y., Kanell, T. A., Xu, P., Khattab, O., & Lam, M. S. (2024) · arXiv · doi:10.48550/arxiv.2402.14207 · Cited by 10 · see citing works
Truthful AI: Developing and governing AI that does not lie
Evans, O., Cotton-Barratt, O., Finnveden, L., Bales, A., Balwit, A., Wills, P., Righetti, L., & Saunders, W. (2021) · arXiv · doi:10.48550/arxiv.2110.06674 · Cited by 39 · see citing works
Efficient Fine-Tuning Large Language Models for Knowledge-Aware Response Planning
Nguyen, M., Kishan, K. C., Nguyen, T., Chadha, A., & Vu, T. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43415-0_35 · Cited by 8 · see citing works
Diversify Question Generation with Retrieval-Augmented Style Transfer
Gou, Q., Xia, Z., Yu, B., Yu, H., Huang, F., Li, Y., & Cam-Tu, N. (2023) · arXiv · doi:10.48550/arxiv.2310.14503 · Cited by 8 · see citing works
The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models
Hagström, L., Saynova, D., Norlund, T., Johansson, M., & Johansson, R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.332 · Cited by 6 · see citing works
Improving Generative Ai Reliability: A Qualitative Study of Hallucination in Large Language Models
Gwon, H. J., Yoo, Y., & Lee, J. Y. (2025) · doi:10.2139/ssrn.5118420 · Cited by 4 · see citing works
Evaluating the Long-Term Memory of Large Language Models
Jia, Z., Liu, Q., Li, H., Chen, Y., & Liu, J. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.1014 · Cited by 5 · see citing works
“Although long-context strategies (Bertsch et al., 2024) and retrieval-augmented generation techniques (Shuster et al., 2021) have improved LLMs’ memory in handling long-term tasks, these text-based memory methods face significant limitations in terms of token count, computational cost, and…”
Evaluating Very Long-Term Conversational Memory of LLM Agents
Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · arXiv · doi:10.48550/arxiv.2402.17753 · Cited by 3 · see citing works
“Multimodal dialog is enabled with image-sharing and image-response behaviors. techniques (Shuster et al., 2021; Ram et al., 2023; Shi et al., 2023), there is still a need for thorough evaluation of their efficacy in handling very long conversations.”
BackgroundHallE-Control: Controlling Object Hallucination in Large Multimodal Models
Zhai, B., Yang, S., Xu, C., Shen, S., Keutzer, K., Li, C., & Li, M. (2023) · arXiv · doi:10.48550/arxiv.2310.01779 · Cited by 2 · see citing works
Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA
Huang, W., Zhou, G., Wang, H., Vougiouklis, P., Lapata, M., & Pan, J. Z. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.927 · Cited by 2 · see citing works
Do as We Do, Not as You Think: the Conformity of Large Language Models
Weng, Z., Chen, G., & Wang, W. (2025) · arXiv · doi:10.48550/arxiv.2501.13381 · Cited by 1 · see citing works
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
Wu, S., Jiang, O., Zhao, Y., Hu, T., Ma, Y., Zhang, K., Patwardhan, M., & Cohan, A. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1504 · Cited by 1 · see citing works
Jotie, A. A., & Mitra, P. (2026) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3hbm9z4okcrx
Selective Forgetting: A Graph-Based Memory Framework for Long-Term LLM Agents
Rusu, T., Khanzadeh, S., & Alalfi, M. (2026) · arXiv · doi:10.48550/arxiv.2608.28978
“Dense retrieval Karpukhin et al. [2020] and jointly pre-trained retrieval-reading models such as REALM Guu et al. [2020] and RETRO Borgeaud et al. [2022] improved retrieval quality on scale, while retrieval has been shown to reduce hallucinations in dialogue Shuster et al. [2021].”
Shanivendra, A. C. (2026) · arXiv · doi:10.48550/arxiv.2603.14170
AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
Aghaebrahimian, A. (2025) · arXiv · doi:10.48550/arxiv.2512.03634
“Large Language Models (LLMs) have revolutionized various natural language generation tasks, including question answering (Deutsch et al., 2021), text summarization (Goyal et al., 2023), and dialogue systems (Shuster et al., 2021).”
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
- Authors
- Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J.
- Affiliations
- Meta (United States) (US)
- ORCID
- Poff, Spencer
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Published
- 2021
- Pages
- 3784-3803
- OpenAlex
- W3155807546
- Cited by
- 575
- References
- 16
- Citations by year
- 2026: 148 · 2025: 166 · 2024: 105 · 2023: 84 · 2022: 54 · 2021: 18
- FWCI
- 30.14
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, 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 › Information Systems › Information Retrieval and Search Behavior
- Keywords
- retrieval-augmented generation, knowledge-grounded dialogue, neural retrieval, rankers, encoder-decoder architecture, chatbots, factual accuracy, multi-turn dialogue, open-domain conversation
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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