Cited by 199Speech and dialogue systemsTopic ModelingInformation Retrieval and Search Behavior

The DOI matches its Crossref record; 46 of the 48 references it cites were found in a registry or an index.

Internet-Augmented Dialogue Generation

Open access

Komeili, Mojtaba, Shuster, Kurt, Weston, Jason·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

Open this paper in CoChat — free account Open PDF

Abstract

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).

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.18653/v1/2022.acl-long.579

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Pointer Sentinel Mixture ModelsPreprint
Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07843 · Cited by 5,058
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546
Self-citation
The Pushshift Reddit DatasetPreprint
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · International Conference on Web and Social Media · doi:10.48550/arxiv.2001.08435 · Cited by 1,185

Show all 48 references

Research works printed but not linked: 3

Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2021. Nearest neigh- bor machine translation. In International Conference on Learning Representations.
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017. Pointer sentinel mixture mod- els. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. Open- Review.net.
Jason Weston, Sumit Chopra, and Antoine Bordes. 2015. Memory networks. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceed- ings.

Sorted by citations

Toolformer: Language Models Can Teach Themselves to Use Tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023) · arXiv · doi:10.48550/arxiv.2302.04761 · Cited by 1,150
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671470 · Cited by 827
Sustainable AI: Environmental Implications, Challenges and Opportunities
Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Behram, F. A., Huang, J., Bai, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.00364 · Cited by 405
Data augmentation approaches in natural language processing: A survey
Li, B., Hou, Y., & Che, W. (2022) · AI Open · doi:10.1016/j.aiopen.2022.03.001 · Cited by 401
Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering
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
Query Rewriting in Retrieval-Augmented Large Language Models
Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.322 · Cited by 602
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., Yang, L., Zhang, W., Jiang, J., & Cui, B. (2026) · Data Science and Engineering · doi:10.1007/s41019-025-00335-5 · Cited by 871
From Matching to Generation: A Survey on Generative Information Retrieval
Li, X., Jin, J., Zhou, Y., Zhang, Y., Zhang, P., Zhu, Y., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3722552 · Cited by 172
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., Chowdhury, T., Li, Y., Cui, H., Zhang, X., et al. (2025) · ACM Computing Surveys · doi:10.1145/3764579 · Cited by 385
CLIP in medical imaging: A survey
Zhao, Z., Liu, Y., Wu, H., Wang, M., Li, Y., Wang, S., Teng, L., Liu, D., Cui, Z., Wang, Q., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103551 · Cited by 248
Knowledge Unlearning for Mitigating Privacy Risks in Language Models
Jang, J., Yoon, D., Yang, S., Cha, S., Lee, M., Logeswaran, L., & Seo, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.805 · Cited by 349
Information Retrieval meets Large Language Models: A strategic report from Chinese IR community
Ai, Q., Bai, T., Cao, Z., Chang, Y., Chen, J., Chen, Z., Cheng, Z., Dong, S., Dou, Z., Feng, F., et al. (2023) · AI Open · doi:10.1016/j.aiopen.2023.08.001 · Cited by 115
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Iyer, S., Lin, X. V., Pasunuru, R., Mihaylov, T., Simig, D., Yu, P., Shuster, K., Wang, T., Liu, Q., Koura, P. S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.12017 · Cited by 81
Self-citation
“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset
Smith, E. M., Hall, M., Kambadur, M., Presani, E., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.625 · Cited by 97
Between reality and delusion: challenges of applying large language models to companion robots for open-domain dialogues with older adults
Irfan, B., Kuoppamäki, S., Hosseini, A., & Skantze, G. (2025) · Autonomous Robots · doi:10.1007/s10514-025-10190-y · Cited by 92
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
Shuster, K., Komeili, M., Adolphs, L., Roller, S., Szlam, A. D., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.27 · Cited by 118
Self-citation
Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview
Han, B., Susnjak, T., & Mathrani, A. (2024) · Applied Sciences · doi:10.3390/app14199103 · Cited by 68
LLMIF: Augmented Large Language Model for Fuzzing IoT Devices
Wang, J., Yu, L., & Luo, X. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00211 · Cited by 75
PrivacyAsst: Safeguarding User Privacy in Tool-Using Large Language Model Agents
Zhang, X., Xu, H., Ba, Z., Wang, Z., Hong, Y., Liu, J., Qin, Z., & Ren, K. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3372777 · Cited by 73
ProsocialDialog: A prosocial backbone for conversational agents
Kim, H., Yu, Y., Jiang, L., Lu, X., Khashabi, D., Kim, G., Choi, Y., & Sap, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.267 · Cited by 80
Generative Spoken Dialogue Language Modeling
Nguyen, T. A., Kharitonov, E., Copet, J., Adi, Y., Hsu, W.-N., Elkahky, A. M., Tomasello, P., Algayres, R., Sagot, B., Mohamed, A., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00545 · Cited by 97
CRITIC: 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 561
“, 2020) or search engines (Nakano et al., 2021; Komeili et al., 2022), enhance math reasoning with a calculator (Andor et al.”
Background
Towards Continual Knowledge Learning of Language Models
Jang, J., Ye, S., Yang, S., Shin, J., Han, J., Kim, G., Choi, S. J., & Seo, M. (2021) · arXiv · doi:10.48550/arxiv.2110.03215 · Cited by 49
DialFact: 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
Exploring the state of the art in legal QA systems
Abdallah, A., Piryani, B., & Jatowt, A. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00802-8 · Cited by 62
Few-Shot Bot: Prompt-Based Learning for Dialogue SystemsPreprint
Madotto, A., Lin, Z., Winata, G. I., & Fung, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08118 · Cited by 96
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
Semnani, S. J., Yao, V., Zhang, H., & Lam, M. S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.157 · Cited by 78
Assisting 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
Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation
Wang, C., Zhang, J., Feng, Y., Li, T., Sun, W., Liu, Y., & Peng, X. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3714462 · Cited by 40
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
Smith, E., Hsu, O., Qian, R., Roller, S., Boureau, Y.-L., & Weston, J. (2022) · Proceedings of the 4th Workshop on NLP for Conversational AI · doi:10.18653/v1/2022.nlp4convai-1.8 · Cited by 40
Self-citation
Towards a comprehensive understanding of the impact of large language models on natural language processing: challenges, opportunities and future directions^†
Huang, X., Liu, Z. Y., Zhang, M., 桂, 韬., 韩, 先., 户, 保., 黄, 民., 黄, 萱., 刘, 康., 刘, 挺., et al. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0113 · Cited by 42
Generalization potential of large language models
Budnikov, M., Bykova, A., & Yamshchikov, I. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10827-6 · Cited by 42
Automatic Evaluation of Attribution by Large Language Models
Yue, X., Wang, B., Chen, Z., Zhang, K., Su, Y., & Sun, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.307 · Cited by 112
Digital forgetting in large language models: a survey of unlearning methods
Blanco-Justicia, A., Jebreel, N., Manzanares-Salor, B., Sánchez, D., Domingo-Ferrer, J., Collell, G., & Eeik Tan, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11078-6 · Cited by 31
RankGen: Improving Text Generation with Large Ranking Models
Krishna, K., Chang, Y., Wieting, J., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.15 · Cited by 30
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Baek, J., Chandrasekaran, N., Cucerzan, S., Herring, A., & Jauhar, S. K. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645404 · Cited by 57
Learning to automate follow-up question generation using process knowledge for depression triage on reddit posts
Gupta, S., Agarwal, A., Gaur, M., Roy, K., Narayanan, V., Kumaraguru, P., & Sheth, A. (2022) · Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology · doi:10.18653/v1/2022.clpsych-1.12 · Cited by 27
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zhang, Z., Fang, M., Chen, L., Namazi‐Rad, M., & Wang, Y.-X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.516 · Cited by 66
BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature
Frisoni, G., Mizutani, M., Moro, G., & Valgimigli, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.390 · Cited by 29
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
Liang, C., Deng, Y., Bian, Y., Qin, Z., Wu, B., Chua, T., & Wong, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.390 · Cited by 56
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 37
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
Self-citation
Text data augmentation for large language models: a comprehensive survey of methods, challenges, and opportunities
Chai, Y., Xie, H., & Qin, J. S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11405-5 · Cited by 57
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 37
FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
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
“For example, Press et al. (2022); Komeili et al. (2022) gathered information from the Internet to enhance question answering and dialog systems, respectively.”
Background
CREATOR: 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
Prompting large language models with knowledge graphs for question answering involving long-tail facts
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 29
Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
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 36
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 37
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 24
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
Self-citation
X-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 24
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
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 23
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 20
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 18
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 16
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
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 37
Self-citation
Query 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
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 18
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 29
Infusing internalized knowledge of language models into hybrid prompts for knowledgeable dialogue generation
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 13
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 19
Natural conversations with a virtual being: How user experience with a current conversational AI model compares to expectations
So, C., Khvan, A., & Choi, W. (2023) · Computer Animation and Virtual Worlds · doi:10.1002/cav.2149 · Cited by 16
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
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
(2022) · doi:10.18653/v1/2022.clpsych-1 · Cited by 12
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
The rapid competitive economy of machine learning development: a discussion on the social risks and benefits
Walter, Y. (2023) · AI and Ethics · doi:10.1007/s43681-023-00276-7 · Cited by 14
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 16
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
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 20
Automatic Detection of Inconsistencies and Hierarchical Topic Classification for Open-Domain Chatbots
Rodríguez-Cantelar, M., Estecha-Garitagoitia, M., D’Haro, L. F., Matía, F., & de Córdoba, R. (2023) · Applied Sciences · doi:10.3390/app13169055 · Cited by 11
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 14
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
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 12
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
GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews
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 13
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 17
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 13
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
Development of dialogue system architecture toward co-creating social intelligence when talking with a partner robot
Fujii, A., Jokinen, K., Okada, K., & Inaba, M. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.933001 · Cited by 9
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 18
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 30
Self-citation
Automatic Diary Generation System Including Information on Joint Experiences Between Humans and Robots
Ichikura, 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
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 11
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 12
UniRQR: A Unified Model for Retrieval Decision, Query, and Response Generation in internet-based knowledge dialogue systems
Hu, Z., Chen, Y., Zhao, M., Li, R., & Wang, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126494 · Cited by 9
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction
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 20
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 9
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
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 11
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
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 7
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
AfriWOZ: Corpus for Exploiting Cross-Lingual Transfer for Dialogue Generation in Low-Resource, African Languages
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 12
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 6
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
Redefining digital health interfaces with large language models
Imrie, F., Rauba, P., & van der Schaar, M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1623339 · Cited by 11
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 7
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

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)
Publisher: Association for Computational Linguistics
Published: 2022
Pages: 8460-8478
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2022.acl-long.579
OpenAlex: W3186138538
Cited by: 192
References: 48
Citations by year: 2026: 13 · 2025: 30 · 2024: 35 · 2023: 74 · 2022: 33
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 ›

Are you an author, or do you see a problem with this page? Request a review

Internet-Augmented Dialogue Generation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks