Beyond Goldfish Memory: Long-Term Open-Domain Conversation
Open accessAbstract
Despite recent improvements in open-domain dialogue models, state-of-the-art models are trained and evaluated on short conversations with little context.In contrast, the long-term conversation setting has hardly been studied.In this work we collect and release a humanhuman dataset consisting of multiple chat sessions whereby the speaking partners learn about each other's interests and discuss the things they have learnt from past sessions.We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better.In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state-of-the-art.* We use this term colloquially, see Agranoff et al. (1965) for evidence of goldfish long-term memory.
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Cites
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 · see citing works
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 · see citing works
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 · see citing works
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767 · see citing works
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 · see citing works
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 · see citing works
Longformer: The Long-Document Transformer
Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281 · see citing works
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 · see citing works
Self-citationReformer: The Efficient Transformer
Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020) · arXiv · doi:10.48550/arxiv.2001.04451 · Cited by 1,435 · see citing works
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1050 · Cited by 1,416 · see citing works
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 · see citing works
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177 · see citing works
Self-citationLeveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G., & Grave, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.74 · Cited by 1,109 · see citing works
Generating Long Sequences with Sparse Transformers
Child, R., Gray, S., Radford, A., & Sutskever, I. (2019) · arXiv · doi:10.48550/arxiv.1904.10509 · Cited by 1,063 · see citing works
A Persona-Based Neural Conversation Model
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898 · see citing works
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
Rashkin, H., Smith, E. M., Li, M., & Boureau, Y.-L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1534 · Cited by 883 · see citing works
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., & Blackburn, J. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7347 · Cited by 881 · see citing works
Neural Summarization by Extracting Sentences and Words
Cheng, J., & Lapata, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1046 · Cited by 802 · see citing works
Recipes for Building an Open-Domain Chatbot
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., et al. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.24 · Cited by 784 · see citing works
Self-citationRecipes for building an open-domain chatbot
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Shuster, K., Smith, E. M., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.13637 · Cited by 784
Self-citationDailyDialog: A Manually Labelled Multi-turn Dialogue DatasetPreprint
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.03957 · Cited by 757 · see citing works
Towards a Human-like Open-Domain Chatbot
Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., et al. (2020) · arXiv · doi:10.48550/arxiv.2001.09977 · Cited by 614 · see citing works
Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01241 · Cited by 610 · see citing works
Self-citationRetrieval Augmentation Reduces Hallucination in Conversation
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.320 · Cited by 575 · see citing works
Self-citationSAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Gliwa, B., Mochol, I., Biesek, M., & Wawer, A. (2019) · Proceedings of the 2nd Workshop on New Frontiers in Summarization · doi:10.18653/v1/d19-5409 · Cited by 424 · see citing works
Neural Text Summarization: A Critical Evaluation
Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1051 · Cited by 335 · see citing works
Challenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288 · see citing works
DailyDialog: A Manually Labelled Multi-Turn Dialogue Dataset
Li, Y. (2025) · TIB Data Manager · doi:10.57702/l9x4bl6j · Cited by 262 · see citing works
Training Millions of Personalized Dialogue Agents
Mazaré, P.-E., Humeau, S., Raison, M., & Bordes, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1298 · Cited by 226 · see citing works
Memory fixation in the goldfish.
Agranoff, B. W., Davis, R. E., & Brink, J. J. (1965) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.54.3.788 · Cited by 214 · see citing works
Retrieval Augmentation Reduces Hallucination in ConversationPreprint
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07567 · Cited by 172 · see citing works
Self-citationLearning to Speak and Act in a Fantasy Text Adventure Game
Urbanek, J., Fan, A., Karamcheti, S., Jain, S., Humeau, S., Dinan, E., Rocktäschel, T., Kiela, D., Szlam, A. D., & Weston, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1062 · Cited by 163 · see citing works
Self-citationConversational AI: Dialogue Systems, Conversational Agents, and Chatbots
McTear, M. (2021) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02176-3 · Cited by 155 · see citing works
A Persona-Based Neural Conversation ModelPreprint
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06155 · Cited by 148 · see citing works
Recipes for Safety in Open-domain ChatbotsPreprint
Xu, J., Ju, D., Li, M., Boureau, Y.-L., Weston, J., & Dinan, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07079 · Cited by 130 · see citing works
Self-citationNeural Summarization by Extracting Sentences and WordsPreprint
Cheng, J., & Lapata, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07252 · Cited by 126 · see citing works
Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts
Goo, C.-W., & Chen, Y.-N. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639531 · Cited by 110 · see citing works
Automatic text summarization with neural networks
Kaikhah, K. (2004) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2004.1344634 · Cited by 89 · see citing works
Improving Neural Conversational Models with Entropy-Based Data Filtering
Csáky, R., Purgai, P., & Recski, G. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1567 · Cited by 60 · see citing works
West, P., Holtzman, A., Buys, J., & Choi, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1389 · Cited by 46 · see citing works
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Dinan, E., Abercrombie, G., Bergman, A. S., Spruit, S., Hovy, D., Boureau, Y.-L., & Rieser, V. (2021) · arXiv · doi:10.48550/arxiv.2107.03451 · Cited by 44 · see citing works
Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset
Rameshkumar, R., & Bailey, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.459 · Cited by 37 · see citing works
Dial2Desc: End-to-end Dialogue Description GenerationPreprint
Pan, H., Zhou, J., Zhao, Z., Liu, Y., Cai, D., & Yang, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00185 · Cited by 14 · see citing works
The Gutenberg Dialogue Dataset
Csáky, R., & Recski, G. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.11 · Cited by 13 · see citing works
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G., & Grave, E. (2020) · arXiv · doi:10.48550/arxiv.2007.01282
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Gliwa, B., Mochol, I., Biesek, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1911.12237
Neural Text Summarization: A Critical Evaluation
Kryściński, W., Keskar, N. S., McCann, B., et al. (2019) · arXiv · doi:10.48550/arxiv.1908.08960
Improving Neural Conversational Models with Entropy-Based Data Filtering
Csaky, R., Purgai, P., & Recski, G. (2019) · arXiv · doi:10.48550/arxiv.1905.05471
The Gutenberg Dialogue Dataset
Csaky, R., & Recski, G. (2020) · arXiv · doi:10.48550/arxiv.2004.12752
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2018) · arXiv · doi:10.48550/arxiv.1810.02508
West, P., Holtzman, A., Buys, J., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1909.07405
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
“[153] introduce a dataset consisting of over 40 utterances per episode on average.”
BackgroundUnleashing the potential of prompt engineering for large language models
Chen, B., Zhang, Z.-F., Langrené, N., & Zhu, S.-X. (2023) · Patterns · doi:10.1016/j.patter.2025.101260 · Cited by 462 · see citing works
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
MemoryBank: Enhancing Large Language Models with Long-Term Memory
Zhong, W., Guo, L., Gao, Q., Ye, H., & Wang, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.10250 · Cited by 253 · see citing works
Jo, E., Epstein, D. A., Jung, H., & Kim, Y.-H. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581503 · Cited by 174 · see citing works
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Zamani, H., Trippas, J. R., Dalton, J., & Radlinski, F. (2023) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000081 · Cited by 124 · see citing works
A Survey on the Memory Mechanism of Large Language Model-based Agents
Zhang, Z., Dai, Q., Bo, X., Ma, C., Li, R., Chen, X., Zhu, J., Dong, Z., & Wen, J.-R. (2025) · ACM Transactions on Information Systems · doi:10.1145/3748302 · Cited by 118 · see citing works
“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 · see citing works
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Cho, Y. J., Liu, L., Xu, Z., Fahrezi, A., & Joshi, G. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.717 · Cited by 87 · see citing works
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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 · see citing works
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory
Xu, X., Gou, Z., Wu, W., Niu, Z.-Y., Wu, H., Wang, H., & Wang, S. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.207 · Cited by 80 · see citing works
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 78 · see citing works
Self-citationJo, E., Jeong, Y., Park, S., Epstein, D. A., & Kim, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642420 · Cited by 75 · see citing works
Irfan, B., Kuoppamäki, S., Hosseini, A., & Skantze, G. (2025) · Autonomous Robots · doi:10.1007/s10514-025-10190-y · Cited by 75 · see citing works
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Goodman, S. M., Buehler, E., Clary, P., Coenen, A., Donsbach, A., Horne, T. N., Lahav, M., MacDonald, R., Michaels, R. B., Narayanan, A., et al. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3544819 · Cited by 71 · see citing works
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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
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Mestre, R., Milicin, R., Middleton, S. E., Ryan, M. E., Zhu, J., & Norman, T. J. (2021) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/2021.argmining-1.8 · Cited by 20 · see citing works
Survey and perspective on social emotions in robotics
Hieida, C., & Nagai, T. (2022) · Advanced Robotics · doi:10.1080/01691864.2021.2012512 · Cited by 18 · see citing works
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches, and Future Directions
Jia, Q., Liu, Y., Ren, S., & Zhu, K. Q. (2023) · ACM Computing Surveys · doi:10.1145/3622933 · Cited by 17 · see citing works
Ehtesham-Ul-Haque, M., D’Rozario, J., Adnin, R., Utshaw, F. T., Tasneem, F., Shefa, I. J., & Islam, A. B. M. A. A. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101168 · Cited by 17 · see citing works
Complex QA and language models hybrid architectures, SurveyPreprint
Daull, X., Bellot, P., Bruno, E., Martin, V., & Murisasco, É. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.09051 · Cited by 17 · see citing works
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Tan, Z., Yan, J., Hsu, I.-H., Han, R., Wang, Z., Le, L., Song, Y., Chen, Y., Palangi, H., Lee, G., et al. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.413 · Cited by 16 · see citing works
Prompting for Socially Intelligent Agents with ChatGPT
Antunes, A. S., Campos, J., Guimarães, M., Dias, J., & Santos, P. A. (2023) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3570945.3607303 · Cited by 16 · see citing works
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
Zhang, T., Liu, Y., Li, B., Zeng, Z., Wang, P., You, Y., Miao, C., & Cui, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.247 · Cited by 14 · see citing works
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming
Gao, J., Lian, Y., Zhou, Z., Fu, Y., & Wang, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.858 · Cited by 15 · 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
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues
Wang, H., Hu, M., Deng, Y., Wang, R., Mi, F., Wang, W., Wang, Y., Kwan, W.-C., King, I., & Wong, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.641 · Cited by 14 · see citing works
FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation
Zhang, C., D’Haro, L. F., Zhang, Q., Friedrichs, T., & Li, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.220 · Cited by 14 · see citing works
The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialogue
Skantze, G., & Doğruöz, A. S. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.57 · Cited by 14 · see citing works
Cox, S. R., Lee, Y., & Ooi, W. T. (2023) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3623809.3623875 · Cited by 14 · see citing works
Jang, J., Boo, M., & Kim, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.838 · Cited by 13 · see citing works
A Safe Collaborative Chatbot for Smart Home Assistants
Chkroun, M., & Azaria, A. (2021) · Sensors · doi:10.3390/s21196641 · Cited by 13 · 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
MPCHAT: Towards Multimodal Persona-Grounded Conversation
Ahn, J., Song, Y., Yun, S., & Kim, G. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.189 · Cited by 13 · see citing works
Troussas, C., Krouska, A., Phivos Mylonas, B., Sgouropoulou, C., & Voyiatzis, I. (2025) · Computers · doi:10.3390/computers14030089 · Cited by 12 · 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
Leveraging Large Language Models for Automated Dialogue Analysis
Finch, S. E., Paek, E. S., & Choi, J. D. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.20 · Cited by 12 · see citing works
Yan, S., Yang, X., Huang, Z., Nie, E., Ding, Z., Li, Z., Ma, X., Bi, J., Kersting, K., Pan, J. Z., et al. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.583 · Cited by 10 · 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-citationDon’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
Multi-perspective empathy modeling for empathetic dialogue generation
Qiao, B., Zhang, Y., Gao, P., Li, X., Wang, S., & Han, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113191 · Cited by 9 · see citing works
A Persona-Aware LLM-Enhanced Framework for Multi-Session Personalized Dialogue Generation
Liu, D., Wu, Z., Song, D., & Huang, H. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.5 · Cited by 9 · see citing works
Ai, K., Tang, K., & Wang, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3633888 · Cited by 9 · see citing works
Revolutionizing Customer Service : The Impact of Large Language Models on Chatbot Performance
Meduri, S. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241051057 · Cited by 9 · see citing works
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Chen, M., Shi, W., Yan, F., Hou, R., Zhang, J., Sahay, S., & Yu, Z. (2022) · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP) · doi:10.18653/v1/2022.aacl-main.31 · Cited by 9 · 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
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Zheng, C., Zhou, J., Zheng, Y., Peng, L., Guo, Z., Wu, W., Niu, Z.-Y., Wu, H., & Huang, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.2 · Cited by 9 · see citing works
Users' Expectations and Practices with Agent Memory
Jones, B., Stemmler, K., Su, E., Kim, Y., & Kuzminykh, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720158 · Cited by 8 · see citing works
Tao, D., Wang, C., Huang, F., Chen, J., Huang, Y., & Jiang, M. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114011 · Cited by 8 · 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
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
Shi, W., Dinan, E., Shuster, K., Weston, J., & Xu, J. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.169 · Cited by 8 · see citing works
Self-citationZep: A Temporal Knowledge Graph Architecture for Agent Memory
Rasmussen, P., Paliychuk, P., Beauvais, T., Ryan, J., & Chalef, D. (2025) · arXiv · doi:10.48550/arxiv.2501.13956 · Cited by 9 · see citing works
“The first evaluation employs the Deep Memory Retrieval (DMR) task developed in [3], which uses a 500-conversation subset of the Multi-Session Chat dataset introduced in "Beyond Goldfish Memory: Long-Term Open-Domain Conversation" [22].”
Towards lifelong dialogue agents via timeline-based memory management
Ong, K. T.-I., Kim, N., Gwak, M., Chae, H., Kwon, T., Jo, Y., Hwang, S.-W., Lee, D., & Yeo, J. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.435 · Cited by 18 · see citing works
OECD AI Capability Indicators Technical Report
OECD (2025) · OECD eBooks · doi:10.1787/9cdb3dd1-en · Cited by 7 · see citing works
Draw Me a Flower: Processing and Grounding Abstraction in Natural Language
Lachmy, R., Pyatkin, V., Manevich, A., & Tsarfaty, R. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00522 · Cited by 7 · see citing works
Jo, W., Hong, S., Choi, J.-W., Kwon, B., Sang, H., Im, D., Kim, S., Kim, S., Lee, T. D., & Yoo, H. (2025) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc49661.2025.10904658 · 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
Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Zhang, S., Ma, R., Ma, Y., Li, S., Xu, Y., Yi, X., & Li, H. (2025) · Proceedings of the 2025 Workshop on Human-Centered AI Privacy and Security · doi:10.1145/3733816.3760750 · Cited by 6 · see citing works
Non-Real-Time Chatbot: Improving Anthropomorphism and Realizing Long-Term Human–Chatbot Interaction
Mitsuno, S., Ban, M., Arimoto, T., Sugiyama, H., Ishiguro, H., & Yoshikawa, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2508316 · Cited by 6 · 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
Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
Zhang, Q., Naradowsky, J., & Miyao, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.720 · Cited by 6 · see citing works
Du, Y., Wang, H., Zhao, Z., Liang, B., Wang, B., Zhong, W., Wang, Z., & Wong, K. (2024) · SIGHAN Workshop on Chinese Language Processing (SIGHAN) · doi:10.18653/v1/2024.sighan-1.18 · Cited by 6 · see citing works
Compound AI System for Personalized Learning: Integrating LLM Agents with Knowledge Graphs
Antonov, E. (2024) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai64321.2024.10911764 · Cited by 6 · see citing works
Kwon, D., Lee, S., Kim, K. H., Lee, S., Kim, T.-Y., & Davis, E. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.68 · Cited by 6 · see citing works
Leveraging Intent Entity Enhancement for Task-Oriented Dialogue
Chen, J., Li, S., Kan, B., Wang, F. L., & Hao, T. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10649940 · Cited by 6 · see citing works
Automatically Exposing Problems with Neural Dialog Models
Yu, D., & Sagae, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.37 · Cited by 6 · see citing works
Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge
Lee, Y., Lee, D., Youn, J., Oh, K.-J., Ko, B., Hyeon, J., & Choi, H. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.708 · Cited by 5 · see citing works
Improving the closing sequences of interaction between human and robot through conversation analysis
Uchida, T., Kameo, N., & Ishiguro, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-81353-7 · Cited by 5 · see citing works
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
Rethinking Response Evaluation from Interlocutor’s Eye for Open-Domain Dialogue Systems
Tsuta, Y., Yoshinaga, N., Sato, S., & Toyoda, M. (2023) · doi:10.18653/v1/2023.ijcnlp-srw.8 · Cited by 5 · see citing works
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 · 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
- Authors
- Xu, J., Szlam, A., & Weston, J.
- Affiliations
- Meta (United States) (US)
- ORCID
- Xu, Jing
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 5180-5197
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3186804217
- Cited by
- 188
- References
- 42
- Citations by year
- 2026: 33 · 2025: 30 · 2024: 32 · 2023: 51 · 2022: 36 · 2021: 6
- FWCI
- 19.38
- 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, public-domain)
- 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 › Artificial Intelligence › AI in Service Interactions
- Keywords
- open-domain dialogue, long-context models, retrieval-augmented generation, dialogue summarization, chatbot evaluation
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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