Internet-Augmented Dialogue Generation
Open accessAbstract
The largest store of continually updating knowledge on our planet can be accessed via internet search.In this work we study giving access to this information to conversational agents.Large language models, even though they store an impressive amount of knowledge within their weights, are known to hallucinate facts when generating dialogue (Shuster et al., 2021); moreover, those facts are frozen in time at the point of model training.In contrast, we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information.We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledge-driven discussions in order to ground their responses.We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020b).
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An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration
Li, Y., Zhang, R., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72344-5_17 · Cited by 29 · 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-citationChai, Y., Xie, H., & Qin, J. S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11405-5 · Cited by 34 · see citing works
Opportunities and Challenges in Neural Dialog Tutoring
Mačina, J., Daheim, N., Wang, L., Sinha, T., Kapur, M., Gurevych, I., & Sachan, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.173 · Cited by 29 · see citing works
Chern, I.-C., Chern, S., Chen, S., Yuan, W., Feng, K., Zhou, C., He, J., Neubig, G., & Liu, P. (2023) · arXiv · doi:10.48550/arxiv.2307.13528 · Cited by 29 · see citing works
“For example, Press et al. (2022); Komeili et al. (2022) gathered information from the Internet to enhance question answering and dialog systems, respectively.”
BackgroundCREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
Qian, C., Han, C., Fung, Y., Qin, Y., Liu, Z., & Ji, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.462 · Cited by 24 · see citing works
Huang, W., Zhou, G., Lapata, M., Vougiouklis, P., Montella, S., & Pan, J. Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113648 · Cited by 26 · see citing works
Zhang, Q., Naradowsky, J., & Miyao, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.417 · Cited by 26 · see citing works
ShapefileGPT: a multi-agent large language model framework for automated shapefile processing
Lin, Q., Hu, R., Li, H., Wu, S., Li, Y., Fang, K., Feng, H., Du, Z., & Xu, L. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2577884 · Cited by 21 · see citing works
State-of-the-Art in Open-Domain Conversational AI: A Survey
Adewumi, T., Liwicki, F., & Liwicki, M. (2022) · Information · doi:10.3390/info13060298 · 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-citationX-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation
van Sonsbeek, T., & Worring, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_36 · Cited by 20 · see citing works
Q-TOD: A Query-driven Task-oriented Dialogue System
Tian, X., Lin, Y., Song, M., Bao, S., Wang, F., He, H., Sun, S., & Wu, H. Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.489 · Cited by 19 · see citing works
Towards an open-domain chatbot for language practice
Tyen, G., Brenchley, M., Caines, A., & Buttery, P. J. (2022) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2022.bea-1.28 · Cited by 20 · see citing works
Towards Improving the Reliability and Transparency of ChatGPT for Educational Question Answering
Wu, Y., Henriksson, A., Duneld, M., & Nouri, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42682-7_32 · Cited by 18 · see citing works
Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions
Toukmaji, C., & Tee, A. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31210 · Cited by 17 · see citing works
Search-engine-augmented dialogue response generation with cheaply supervised query production
Wang, A., Song, L., Liu, Q., Mi, H., Wang, L., Tu, Z., Su, J., & Yu, D. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103874 · Cited by 15 · see citing works
Xu, J., Ung, M., Komeili, M., Arora, K., Boureau, Y.-L., & Weston, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.758 · Cited by 25 · see citing works
Self-citationQuery Rewriting for Retrieval-Augmented Large Language Models
Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023) · arXiv · doi:10.48550/arxiv.2305.14283 · Cited by 14 · see citing works
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
Li, Y., Zhao, J., Lyu, M. R.-T., & Wang, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.721 · Cited by 14 · see citing works
Automatic Evaluation and Moderation of Open-domain Dialogue SystemsPreprint
Zhang, C., Sedoc, J., D’Haro, L. F., Banchs, R. E., & Rudnicky, A. I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02110 · Cited by 23 · see citing works
Bai, J., Yan, Z., Zhang, S., Yang, J., Guo, H., & Li, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111874 · Cited by 12 · see citing works
MFAQ: a Multilingual FAQ Dataset
Bruyn, M. D., Lotfi, E., Buhmann, J., & Daelemans, W. M. P. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.1 · Cited by 15 · see citing works
So, C., Khvan, A., & Choi, W. (2023) · Computer Animation and Virtual Worlds · doi:10.1002/cav.2149 · Cited by 13 · see citing works
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
Liu, Z., Xu, J., Lei, Z., Wang, H., Niu, Z.-Y., & Wu, H. J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.73 · Cited by 12 · see citing works
Artificial intelligence (AI) powered chatbots: factors in uptake among early adopters
Ju, B., Stewart, B. J., Park, S., & Walker, J. J. (2025) · Aslib Journal of Information Management · doi:10.1108/ajim-12-2024-0994 · Cited by 11 · see citing works
Walter, Y. (2023) · AI and Ethics · doi:10.1007/s43681-023-00276-7 · Cited by 11 · see citing works
Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems
Gros, D., Li, Y., & Zhou, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.215 · Cited by 14 · 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
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems
Finch, S. E., Finch, J. D., & Choi, J. D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.839 · Cited by 10 · see citing works
Rodríguez-Cantelar, M., Estecha-Garitagoitia, M., D’Haro, L. F., Matı́a, F., & de Córdoba, R. (2023) · Applied Sciences · doi:10.3390/app13169055 · Cited by 10 · see citing works
OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience
Li, M., Peng, B., Gao, J., & Zhang, Z. (2023) · ACM Transactions on the Web · doi:10.1145/3623381 · Cited by 11 · see citing works
Dynamically retrieving knowledge via query generation for informative dialogue generation
Hu, Z., Wang, L., Chen, Y., Liu, Y., Li, R., Zhao, M., Lu, X., & Jiang, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127036 · Cited by 10 · see citing works
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking
Chen, D., Qian, K., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.115 · Cited by 11 · see citing works
PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling
Huang, X., Huang, H., Bao, S., Wang, F., Wu, H. R., & Wang, H. (2021) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2021.nlp4convai-1.14 · Cited by 9 · see citing works
Roy, K., Khandelwal, V., Lookingbill, V., Surana, H., Heckman, H., & Sheth, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30577 · Cited by 9 · see citing works
Champagne: Learning Real-world Conversation from Large-Scale Web Videos
Han, S., Hessel, J., Dziri, N., Choi, Y., & Yu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01421 · Cited by 14 · see citing works
RALLRec+: Retrieval augmented large language model recommendation with reasoning
Luo, S., Xu, J., Zhang, X., Wang, L., Liu, S., Hou, H., & Song, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129508 · Cited by 9 · see citing works
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
Cai, P., Wan, H., Liu, F., Yu, M., Yu, H. Q., & Joshi, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.352 · Cited by 8 · see citing works
Fujii, A., Jokinen, K., Okada, K., & Inaba, M. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.933001 · Cited by 8 · see citing works
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
Liu, Z., Xiao, S., Shao, Y., & Cao, Z. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.148 · Cited by 15 · see citing works
The CRINGE Loss: Learning what language not to model
Adolphs, L., Gao, T., Xu, J., Shuster, K., Sukhbaatar, S., & Weston, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.493 · Cited by 10 · see citing works
Self-citationIchikura, A., Kawaharazuka, K., Obinata, Y., Shinjo, K., Okada, K., & Inaba, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-44981-9_33 · Cited by 8 · see citing works
The Web Can Be Your Oyster for Improving Language Models
Li, J., Tang, T., Zhao, W. X., Wang, J., Nie, J., & Wen, J.-R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.46 · Cited by 8 · see citing works
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Chawla, K., Shi, W., Zhang, J., Lucas, G., Zhou, Y., & Gratch, J. S. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.53 · Cited by 11 · see citing works
Hu, Z., Chen, Y., Zhao, M., Li, R., & Wang, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126494 · Cited by 8 · see citing works
Rakin, S., Shibly, M. A. R., Hossain, Z. M., Akbar, M. M., & Khan, Z. (2025) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-89063-5_42 · Cited by 12 · see citing works
KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
Bai, J., Zhao, Y., Yang, Z., Yang, J., Liang, X., Guo, H., & Li, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43415-0_31 · Cited by 7 · see citing works
Open Source System Integration Towards Natural Interaction with Robots
Fujii, A., & Jokinen, K. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889582 · Cited by 7 · see citing works
Enhancing Protocol Fuzzing via Diverse Seed Corpus Generation
Luo, Z., Du, Q., Wang, Y., Roychoudhury, A., & Jiang, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3595396 · Cited by 8 · see citing works
Generative commonsense knowledge subgraph retrieval for open-domain dialogue response generation
Wu, S., Yu, J., Chen, J., & Zhou, W. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106666 · Cited by 6 · see citing works
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection
Majumder, B. P., Jhamtani, H., Berg-Kirkpatrick, T., & McAuley, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.224 · Cited by 6 · see citing works
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices
Shen, X., Barlacchi, G., Tredici, M. D., Cheng, W., Byrne, B., & de Gispert, A. (2022) · Workshop on e-Commerce and NLP (ECNLP) · doi:10.18653/v1/2022.ecnlp-1.13 · Cited by 6 · see citing works
Adewumi, T., Adeyemi, M., Aremu, A., Peters, B., Buzaaba, H., Oyerinde, S., Rufai, A. M., Ajibade, B., Gwadabe, T., Traore, M. M. K., et al. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191208 · Cited by 9 · see citing works
Retrieval-Augmented Knowledge-Intensive Dialogue
Wang, Z., Gong, P., Zhang, Y., Gu, J., & Yang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_2 · Cited by 5 · see citing works
Requirement Discovery Using Embedded Knowledge Graph with ChatGPT
VanGundy, B. R., Phojanamongkolkij, N., Brown, B. R., Polavarapu, R., & Bonner, J. (2024) · INCOSE International Symposium · doi:10.1002/iis2.13253 · Cited by 5 · see citing works
Redefining digital health interfaces with large language models
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Those Aren’t Your Memories, They’re Somebody Else’s: Seeding Misinformation in Chat Bot Memories
Atkins, C., Zhao, B. Z. H., Asghar, H. J., Wood, I., & Kâafar, M. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33488-7_11 · Cited by 5 · see citing works
Answerability: A custom metric for evaluating chatbot performance
Gupta, P., Rajasekar, A. A., Patel, A., Kulkarni, M., Sunell, A., Kim, K., Ganapathy, K., & Trivedi, A. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.27 · Cited by 6 · see citing works
“What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge
Zhao, C., Gella, S., Kim, S., Jin, D., Hazarika, D., Papangelis, A., Hedayatnia, B., Namazifar, M., Liu, Y., & Hakkani-Tür, D. Z. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.28 · Cited by 5 · see citing works
- Authors
- Komeili, M., Shuster, K., & Weston, J.
- Affiliations
- Meta (United States) (US)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 8460-8478
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3186138538
- Cited by
- 192
- References
- 48
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 35 · 2023: 74 · 2022: 33 · 2021: 7
- FWCI
- 23.78
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- dialogue generation, web search, search query generation, chatbots, hallucinations, large language models, knowledge-grounded dialogue
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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