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 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).
Overview
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
Cites
Statistics
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“These limitations include an inability to access up-to-date information on recent events (Komeili et al., 2022)”
From Toolformer: Language Models Can Teach Themselves to Use Tools (2023) · cites this paper as Komeili et al., 2022
The cited source says what the claim attributes to it.
In the citing paper
“existing approaches either rely on large amounts of human annotations (Komeili et al., 2022;Thoppilan et al., 2022)”
From Toolformer: Language Models Can Teach Themselves to Use Tools (2023) · cites this paper as Komeili et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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-citationThe 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
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.
Cited by
Sorted by citations
Items 101–199 of 199
“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 8
Domain Adaptation for Conversational Query Production with the RAG Model Feedback
Wang, A., Song, L., Ge, X., & Su, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.612 · Cited by 6
Response Enhanced Semi-supervised Dialogue Query Generation
Huang, J., Wang, A., Gao, L., Song, L., & Su, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29790 · Cited by 5
Are Experts Needed? On Human Evaluation of Counselling Reflection Generation
Wu, Z., Balloccu, S., Reiter, E., Helaoui, R., Recupero, D. R., & Riboni, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.382 · Cited by 6
ToolFiVe: Enhancing Tool-Augmented LLMs via Tool Filtering and Verification
Lu, H., Li, X., Ji, X., Kan, Z., & Hu, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887544 · Cited by 6
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
Cai, M., Bao, S., Tian, X., He, H., Wang, F., & Wu, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.97 · Cited by 4
Kadlčík, M., Štefánik, M., Sotolář, O., & Martinek, V. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.742 · Cited by 6
Kumar, S., Bhatia, S., Aggarwal, M., & Chakraborty, T. (2024) · Natural language processing. · doi:10.1017/nlp.2024.42 · Cited by 5
Decoding a Neural Retriever’s Latent Space for Query Suggestion
Adolphs, L., Huebscher, M. C., Buck, C., Girgin, S., Bachem, O., Ciaramita, M., & Hofmann, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.601 · Cited by 9
FluGCF: A Fluent Dialogue Generation Model With Coherent Concept Entity Flow
Zhao, Y., Cheng, B. H., Huang, Y., & Wan, Z. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3340610 · Cited by 4
Building a Knowledge-Based Dialogue System with Text Infilling
Xue, Q., Takiguchi, T., & Ariki, Y. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.25 · Cited by 4
Simulating conversations on social media with generative agent-based models
Jeon, M. S., Mendoza, M., Fernández, M., Providel, E., Rodríguez, F., Espina, N., Carvallo, A., & Abeliuk, A. (2025) · EPJ Data Science · doi:10.1140/epjds/s13688-025-00593-3 · Cited by 5
Overview of the Tenth Dialog System Technology Challenge: DSTC10
Yoshino, K., Chen, Y.-N., Crook, P., Kottur, S., Li, J., Hedayatnia, B., Moon, S., Fei, Z., Li, Z., Zhang, J., et al. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3293030 · Cited by 3
Sun, B., Li, Y., Mi, F., Bie, F., Li, Y., & Li, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.148 · Cited by 3
Knowledge-grounded Dialog State Tracking
Yu, D., Wang, M., Cao, Y., Shafey, L. E., Izhak Shafran, J., & Soltau, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.250 · Cited by 3
q2d: Turning Questions into Dialogs to Teach Models How to Search
Bitton, Y., Cohen-Ganor, S., Hakimi, I., Lewenberg, Y., Aharoni, R., & Weinreb, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.843 · Cited by 7
Mitigating the negative impact of over-association for conversational query production
Wang, A., Song, L., Min, Z., Xu, G., Wang, X., Yao, J., & Su, J. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103907 · Cited by 3
Resolving Unseen Rumors with Retrieval-Augmented Large Language Models
Chen, L., & Wei, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9440-9_25 · Cited by 3
Design Guidelines for Developing Systems for Dialogue System Competitions
Komatani, K., Takeda, R., Nakashima, K., & Nakano, M. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5538-9_11 · Cited by 3
Gradient Ascent Post-training Enhances Language Model Generalization
Yoon, D., Jang, J., Kim, S., & Seo, M. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.74 · Cited by 3
Ueyama, A., & Kano, Y. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.26 · Cited by 3
Frugal Prompting for Dialog Models
Santra, B., Basak, S., De, A., Gupta, M., & Goyal, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.290 · Cited by 3
Wen, J., Zhou, H., Guan, J., Zhou, J., & Huang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.612 · Cited by 3
Park, C., Lee, J., Son, S., Park, K., Jang, J., & Lim, H. (2024) · Applied Sciences · doi:10.3390/app14114821 · Cited by 3
AuToGen: Automated Tool Learning Data Generation with Domain-specific Structured Data
Zeng, D., Zhou, L., Zhang, Z., & Jiang, L. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0005 · Cited by 3
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
Su, H., Kumar, S. H., Mazumder, S., Chen, W., Manuvinakurike, R., Okur, E., Sahay, S., Nachman, L., Chen, S.-T., & Lee, H.-Y. (2023) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2023.dialdoc-1.4 · Cited by 3
Common Law Annotations: Investigating the Stability of Dialog System Output Annotations
Lee, S., DeLucia, A., Nangia, N., Ganedi, P., Guan, R., Li, R., Ngaw, B., Singhal, A., Vaidya, S., Yuan, Z., et al. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.780 · Cited by 3
CyGPT: Knowledge Graph-Based Enhancement Techniques for Large Language Models in Cybersecurity
Ou, L., Ni, X., Wu, W., & Tian, Z. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00036 · Cited by 3
Dial-QP: A Multi-tasking and Keyword-Guided Approach for Enhancing Conversational Query Production
Yu, J., Wu, S., Wang, S., Lai, H., & Zhou, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44693-1_65 · Cited by 2
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation
Ni, X., Dai, H., Ren, Z., & Li, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.771 · Cited by 2
CharmBana: Progressive Responses with Real-Time Internet Search for Knowledge-Powered Conversations
Reddy, R. G., Chandra, S., Bai, H., Yao, W., Sidhu, M., Aggarwal, K., Sonawane, P., & Zhai, C. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635702 · Cited by 2
Lora for dense passage retrieval of ConTextual masked auto-encoding
Zhang, L., Li, S., & Peng, H. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03593-4 · Cited by 2
Automatic Detection of Inconsistencies in Open-Domain Chatbots
Prats, J. M., Estecha-Garitagoitia, M., Rodríguez-Cantelar, M., & D’Haro, L. F. (2022) · IberSPEECH · doi:10.21437/iberspeech.2022-24 · Cited by 2
NewsDialogues: Towards Proactive News Grounded Conversation
Li, S., Yin, Y., Yang, C., Jiang, W., Li, Y., Cheng, Z., Shang, L., Jiang, X., Liu, Q., & Yang, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.224 · Cited by 4
Kobza, O., Herel, D., Čuhel, J., Gargiani, T., Pichl, J., Marek, P., Konrád, J., & Sedivy, J. (2023) · Future Internet · doi:10.3390/fi15120384 · Cited by 2
Wu, B., Deng, Y., Su, D., Xiang, J., Yang, C., Wang, Z., Li, Y., Huang, J., & Wang, B. (2023) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3302231 · Cited by 1
On the Underspecification of Situations in Open-domain Conversational Datasets
Otani, N., Araki, J., Kim, H., & Hovy, E. H. (2023) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2023.nlp4convai-1.2 · Cited by 2
Multi-Source Probing for Open-Domain Conversational Understanding
Li, Y., Zhou, H., Zhou, J., & Huang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.769 · Cited by 1
AtomTool: Empowering Large Language Models with Tool Utilization Skills
Li, Y., Zhang, Z., Zhang, J., Hu, W., Wu, Y., & Hong, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8487-5_23 · Cited by 1
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
Multi-agent Retrieval-Augmented Generation for Enhancing Answer Generation and Knowledge Retrieval
Kumar, D., & Jain, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05179-0_22 · Cited by 3
Enhancing SAP Predictive Maintenance and Industrial Analytics with LLM-Driven Automation
Chandrakumaran, A. (2025) · IEEE Madhya Pradesh Section Conference (MPCON) · doi:10.1109/mpcon66082.2025.11256577 · Cited by 1
Design of an english oral dialogue generation and interaction system assisted by machine learning
Liu, Z., & Tian, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00827-3 · Cited by 1
Shao, Y., Du, Z. Y., Liu, B., Wen, H., Zhang, X., & Zhang, C. (2026) · International Conference on Artificial Intelligence and Social Network Systems (AISNS) · doi:10.1109/aisns67921.2026.11440489 · Cited by 1
Artificial Intelligence-Based Digital Transformation
Volpentesta, T. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80592-096-020261002 · Cited by 1
Investigating User Perceptions of Conversational Agents for Software-related ExploratoryWeb Search
Frazier, M., Kumar, S., Damevski, K., & Pollock, L. (2022) · IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1109/icse-nier55298.2022.9793519 · Cited by 5
Foundation Models for Text Generation
Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_6 · Cited by 3
Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
Liang, X., Du, J., Niu, T., Zhou, L., & Xu, R. (2023) · Applied Sciences · doi:10.3390/app13158707 · Cited by 1
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
Li, M., Peng, B., Galley, M., Gao, J., & Zhang, Z. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.46 · Cited by 1
Zhang, J., Roller, S., Qian, K., Liu, Z., Meng, R., Heinecke, S., Wang, H., Savarese, S., & Xiong, C. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.47 · Cited by 1
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Zhou, W., Li, G., Cheng, X., Liang, X., Zhu, J., Zhai, F., & Li, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.460 · Cited by 6
Stricker, A., & Paroubek, P. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389695 · Cited by 1
Kobza, O., Herel, D., Čuhel, J., Gargiani, T., Marek, P., & Sedivy, J. (2024) · Future Internet · doi:10.3390/fi16090344 · Cited by 2
Is It Smaller Than a Tennis Ball? Language Models Play the Game of Twenty Questions
Bruyn, M. D., Lotfi, E., Buhmann, J., & Daelemans, W. M. P. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.7 · Cited by 2
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 2
ConveRT for FAQ AnsweringPreprint
Bruyn, M. D., Lotfi, E., Buhmann, J., & Daelemans, W. M. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00719 · Cited by 1
Combining Transformers with Natural Language ExplanationsPreprint
Ruggeri, F., Lippi, M., & Torroni, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4531749 · Cited by 2
Studies in conversational AI : multilingual capabilities, world knowledge, and evaluation strategies
Bruyn, M. D. (2024) · doi:10.63028/10067/2023030151162165141 · Cited by 1
Dialogue-Filling: Response Generation Control in Retrieval-Generation Dialogue System
Xue, Q., Takiguchi, T., & Ariki, Y. (2022) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.37-3_ids-c
Sentence Retrieval for Open-Ended Dialogue Using Dual Contextual Modeling
Harel, I., Taitelbaum, H., Szpektor, I., & Kurland, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28244-7_27
From knowledge augmentation to multi-tasking: towards human-like dialogue systems
Yang, T. H. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/168465
Triplet-Free Knowledge-Guided Response Generation
Li, D., Liu, J., & Wang, B. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.815
I already said that! Degenerating redundant questions in open-domain dialogue systems.
Mai, L., & Carson-Berndsen, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-srw.33
On the Compositional Generalization in Versatile Open-domain Dialogue
Fu, T., Zhao, X., Liu, L., & Yan, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.760
Correction of Errors in Preference Ratings from Automated Metrics for Text Generation
Deriu, J., von Däniken, P., Tuggener, D., & Cieliebak, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.404 · Cited by 1
Development and Research of Dialogue Agents with Long-Term Memory and Web Search
Apanasovich, K., Makhnytkina, O. V., & Matveev, Y. N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48309-7_32
DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment
Zhao, Y., Yan, L., Sun, W., Meng, C., Wang, S., Cheng, Z., Ren, Z., & Yin, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1010
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Reddy, R. G., Bai, H., Yao, W., Suresh, S. C. E., Ji, H., & Zhai, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.62
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation
Han, G., Jo, D., Nam, D. W., Yoon, E., Kwon, T., Rho, S., On, K.-W., Yoo, C. D., & Kim, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.177
Continual Learning in Chit-Chat Systems
Mazumder, S., & Liu, B. (2024) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-48189-5_5
Ueyama, A., & Kano, Y. (2024) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.39-3_ids6-e
Wu, Y., & Henriksson, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70352-2_12
Katić, M. A., Čandrlić, S., & Koch, M. R. (2024) · International Conference on Smart Systems and Technologies (SST) · doi:10.1109/sst61991.2024.10755240
A New Metric for Lowering Hallucinations in Large Language Models
Kumar, A., Mishra, D., Raj, H., & Tomar, Y. (2024) · Innovations in Sustainable Technologies and Computing · doi:10.1007/978-981-97-7831-7_38
MM-CARP: Multimodal Model with Cross-Modal Retrieval-Augmented and Visual Region Perception
Guo, J., Fu, C.-Q., Wang, G., Lu, R., Chen, D., & Tang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-2061-6_31
Myo, S. W., & Anutariya, C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-90295-6_11
Effective Content Moderation Using Modern AI Tools
Bortnyk, K., Bahniuk, N., Кондіус, І., Melnyk, K., Melnychuk, Y., & Kondius, K. (2024) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert65323.2024.11122238
Kalyan Chakravarthy, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5393117
Chenguang, W. A. N. G., Ying, W. A. N. G., Yunwei, W. E. N., Zhang, F., Tian, Y., Wang, H., & Zhang, G. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2024-0224
RJAG: Retrieval Judgment Augmented Generation
Wang, K., Hu, Z., Ren, M., & Tao, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2725-0_15
Knowledge as Trust (Amanah): An Islamic Theological Perspective on Information Accessibility
Ritonga, A. R., Purba, A. M., & Rangkuti, Z. A. (2025) · Pharos Journal of Theology · doi:10.46222/pharosjot.106.528
Luo, X., Huo, Y., Xiao, Y., & Yu, X. (2025) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1109/aiahpc66801.2025.11290242
Research on Improving Intimacy with Robot's Diary from Joint Experiences with Humans
Ichikura, A., Okada, K., & Inaba, M. (2025) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.43.911
MRM: Improving Multihop Reasoning Capability for LLMs With Multistage QA Retrieval
Li, W., Zhang, Y., Sun, G., Ma, C., Qiang, J., & Shen, J. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3642285
Virtual Assistant System in Banking Application With Fine-Tuned LLM and RAG Approach
Gunawan, E., Amadeo, A. J., Kamardi, C., & Suhartono, D. (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11327124 · Cited by 1
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
Tang, H., Liu, Y., Zhao, X., Liu, X., Zhang, Y., Zhang, K., Zhou, X., & Chen, E. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00082 · Cited by 1
Wang, Z., & Chen, W. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_2
DTopAgent: A Multi-Agent Framework for Dynamic Top-K Chunk Retrieval in Rag Pipeline
Wang, S., Liu, C., Duan, J., Zhao, Z., Yi, Z., & Li, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464642
(131810), L. Z., Zhang, Z., Rong, H., Liu, Y., Yan, Z., Li, Y., & Chen, H. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-8318-8_28
TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models
Zhang, L., Li, X., & Dou, Z. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809710
DEVELOPMENT OF A RAG-BASED SCHOLARLY RESEARCH CHATBOT WITH FINE-TUNED DENSE RETRIEVAL
Oguntimilehin, A., & Balogun, O. (2026) · Nigerian Journal of Technology · doi:10.67358/njt.2026.5975
A Hybrid Memory Framework for Mitigating Contextual Amnesia in Conversational AI
Rao, V. B., Reddy, P. G. S., Amirthalakshmi.T.M, & G, R. S. (2026) · International Conference on Power, Control and Computing Technologies (ICPC2T) · doi:10.1109/icpc2t68221.2026.11646427
Lyu, B., Tang, C., Yu, G.-X., Liu, N.-Y., & Yu, Y. (2026) · Journal of Computer Science and Technology · doi:10.1007/s11390-026-5767-y
OCTOPUS: Multi-agentic Universal Compositional Visual Retrieval
Cheng, Z., Zheng, B., Zhong, T., & Zhou, F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37023-5_29
Analyzing Responsible Usage of Large Language Models: A Comprehensive Study
Joshi, L., Taneja, K., & Taneja, H. (2026) · International Conference on Computational Intelligence: Methods, Models, and Real-World Applications (ICCIMMR) · doi:10.1109/iccimmr71310.2026.11731698
Metadata
- 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
- 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
