Recipes for Building an Open-Domain Chatbot

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

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 · see citing works

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. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816 · 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

Scaling Laws for Neural Language Models

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · cited 1× · see citing works

Reformer: The Efficient Transformer

Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020) · arXiv · doi:10.48550/arxiv.2001.04451 · Cited by 1,435 · see citing works

Adafactor: Adaptive Learning Rates with Sublinear Memory CostPreprint

Shazeer, N., & Stern, M. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1804.04235 · Cited by 1,410 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · 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

A Deep Reinforced Model for Abstractive Summarization

Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182 · see citing works

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al. (2018) · arXiv · doi:10.48550/arxiv.1811.06965 · Cited by 1,180 · see citing works

Mixed Precision Training

Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al. (2017) · arXiv · doi:10.48550/arxiv.1710.03740 · Cited by 1,179 · 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

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019) · arXiv · doi:10.48550/arxiv.1909.08053 · Cited by 893 · see citing works

The Pushshift Reddit Dataset

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

The design and implementation of Xiaoice, an empathetic social chatbot

Zhou, L., Gao, J., Li, D., & Shum, H.-Y. (2020) · Computational Linguistics · doi:10.1162/coli_a_00368 · Cited by 777 · see citing works

CTRL: A Conditional Transformer Language Model for Controllable Generation

Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., & Socher, R. (2019) · arXiv · doi:10.48550/arxiv.1909.05858 · Cited by 751 · see citing works

From Eliza to XiaoIce: Challenges and opportunities with social chatbots

Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · 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

Transformer-XL: Attentive Language Models Beyond a Fixed-Length ContextPreprint

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., & Salakhutdinov, R. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.ecrts.2023.7 · Cited by 547 · see citing works

Untitled work

(2020) · arXiv (Cornell University) · Cited by 461 · see citing works

TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational AgentsPreprint

Wolf, T., Sanh, V., Chaumond, J., & Delangue, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08149 · Cited by 404 · see citing works

The Second Conversational Intelligence Challenge (ConvAI2)

Dinan, E., Logacheva, V., Malykh, V. A., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A. D., Serban, I. V., Lowe, R., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_7 · Cited by 349 · see citing works

fairseq: A Fast, Extensible Toolkit for Sequence ModelingPreprint

Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01038 · Cited by 340 · see citing works

ParlAI: A Dialog Research Software Platform

Miller, A., Feng, W., Batra, D., Bordes, A., Fisch, A., Lu, J., Parikh, D., & Weston, J. (2017) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d17-2014 · Cited by 326 · see citing works

Experience Grounds Language

Bisk, Y., Holtzman, A., Thomason, J., Andreas, J., Bengio, Y., Chai, J. Y., Lapata, M., Lazaridou, A., May, J., Nisnevich, A., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.703 · Cited by 325 · see citing works

DialoGPT: Large-Scale Generative Pre-training for Conversational Response GenerationPreprint

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00536 · Cited by 257 · 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

Learning to Write with Cooperative Discriminators

Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1152 · Cited by 192 · see citing works

Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills

Smith, E. M., Williamson, M., Shuster, K., Weston, J., & Boureau, Y.-L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.183 · Cited by 179 · see citing works

Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

Humeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 171 · see citing works

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Weston, J., Dinan, E., & Miller, A. (2018) · Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI · doi:10.18653/v1/w18-5713 · Cited by 165 · see citing works

Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

Hancock, B., Bordes, A., Mazare, P.-E., & Weston, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1358 · Cited by 161 · see citing works

Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack

Dinan, E., Humeau, S., Chintagunta, B., & Weston, J. (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/d19-1461 · Cited by 161 · see citing works

Learning 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 158 · see citing works

Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

Dinan, E., Fan, A., Williams, A., Urbanek, J., Kiela, D., & Weston, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.656 · Cited by 157 · 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

Learning Semantic Textual Similarity from Conversations

Yang, Y., Yuan, S., Cer, D. M., Kong, S.-Y., Constant, N., Pilar, P., Ge, H., Sung, Y.-H., Strope, B. P., & Kurzweil, R. (2018) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w18-3022 · Cited by 148 · see citing works

Building a Conversational Agent Overnight with Dialogue Self-PlayPreprint

Shah, P., Hakkani-Tür, D. Z., Tür, G., Rastogi, A., Bapna, A., Nayak, N., & Heck, L. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04871 · Cited by 147 · see citing works

Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

Li, M., Roller, S., Kulikov, I., Welleck, S., Boureau, Y.-L., Cho, K., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.428 · Cited by 128 · see citing works

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

Li, Z., Wallace, E., Shen, S., Lin, K., Keutzer, K., Klein, D., & Gonzalez, J. E. (2020) · International Conference on Machine Learning · Cited by 125 · see citing works

Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning

Shah, P., Hakkani-Tür, D. Z., Liu, B., & Tür, G. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-3006 · Cited by 118 · see citing works

ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn ComparisonsPreprint

Li, M., Weston, J., & Roller, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03087 · Cited by 111 · see citing works

Neural Text Generation With Unlikelihood Training

Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 106 · see citing works

Dialog-based Language LearningPreprint

Weston, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06045 · Cited by 78 · see citing works

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog SystemsPreprint

Ghandeharioun, A., Shen, J. H., Jaques, N., Ferguson, C., Jones, N. C., Lapedriza, À., & Picard, R. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09308 · Cited by 77 · see citing works

The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents

Shuster, K., Da, J. U., Roller, S., Dinan, E., Boureau, Y.-L., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.222 · Cited by 72 · see citing works

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of TransformersPreprint

Li, Z., Wallace, E., Shen, S., Lin, K., Keutzer, K., Klein, D., & Gonzalez, J. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11794 · Cited by 58 · see citing works

How Decoding Strategies Affect the Verifiability of Generated Text

Massarelli, L., Petroni, F., Piktus, A., Ott, M., Rocktäschel, T., Plachouras, V., Silvestri, F., & Riedel, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.22 · Cited by 40 · see citing works

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38 · see citing works

Learning Robust Dialog Policies in Noisy EnvironmentsPreprint

Fazel-Zarandi, M., Li, S.-W. D., Cao, J. L., Casale, J., Henderson, P., Whitney, D., & Geramifard, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04034 · Cited by 20 · see citing works

Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood TrainingPreprint

Li, M., Roller, S., Kulikov, I., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03860 · Cited by 19 · see citing works

Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response ModelsPreprint

He, T., Liu, J., Cho, K., Ott, M., Liu, B., Glass, J. R., & Peng, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07117 · Cited by 16 · see citing works

Sequence Effects in Crowdsourced Annotations

Mathur, N., Baldwin, T. J., & Cohn, T. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1306 · Cited by 10 · see citing works

I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agentsPreprint

Prabhumoye, S., Li, M., Urbanek, J., Dinan, E., Kiela, D., Weston, J., & Szlam, A. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02878 · Cited by 10 · see citing works

I love your chain mail! Making knights smile in a fantasy game world

Prabhumoye, S., Li, M., Urbanek, J., Dinan, E., Kiela, D., Weston, J., & Szlam, A. D. (2019) · Cited by 2 · see citing works

A Goal-oriented Neural Conversation Model by Self-Play

Wei, W., Le, Q. V., Dai, A. M., & Li, L.-J. (2018) · Cited by 1 · see citing works

Experience Grounds Language

Bisk, Y., Holtzman, A., Thomason, J., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.10151

Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

Dinan, E., Fan, A., Williams, A., et al. (2019) · arXiv · doi:10.48550/arxiv.1911.03842

How Decoding Strategies Affect the Verifiability of Generated Text

Massarelli, L., Petroni, F., Piktus, A., Ott, M., Rocktäschel, T., Plachouras, V., Silvestri, F., & Riedel, S. (2019) · arXiv · doi:10.48550/arxiv.1911.03587

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

Multimodal biomedical AI

Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works

The rise and potential of large language model based agents: a survey

Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970 · see citing works

TruthfulQA: Measuring How Models Mimic Human Falsehoods

Lin, S., Hilton, J., & Evans, O. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.229 · Cited by 904 · see citing works

Deep learning for AI

Bengio, Y., LeCun, Y., & Hinton, G. E. (2021) · Communications of the ACM · doi:10.1145/3448250 · Cited by 676 · see citing works

Retrieval 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

Unleashing the potential of prompt engineering for large language models

CHEN, B. ‐., Zhang, Z., Langrené, N., & Zhu, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101260 · Cited by 764 · see citing works

A Literature Survey of Recent Advances in Chatbots

Caldarini, G., Jaf, S., & McGarry, K. (2022) · Information · doi:10.3390/info13010041 · Cited by 470 · see citing works

Pre-Trained Language Models and Their Applications

Wang, H., Li, J., Wu, H. R., Hovy, E. H., & Sun, Y. (2022) · Engineering · doi:10.1016/j.eng.2022.04.024 · Cited by 404 · see citing works

PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting

Xue, H., & Salim, F. D. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3342137 · Cited by 332 · see citing works

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv · doi:10.48550/arxiv.2210.03350 · Cited by 294 · see citing works

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.378 · Cited by 303 · see citing works

Future directions for chatbot research: An interdisciplinary research agenda

Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247 · see citing works

The power of noise: Redefining retrieval for rag systems

Cuconasu, F., Trappolini, G., Siciliano, F., Filice, S., Campagnano, C., Maarek, Y., Tonellotto, N., & Silvestri, F. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657834 · Cited by 236 · see citing works

Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design

Haugeland, I. K. F., Følstad, A., Taylor, C., & Bjørkli, C. A. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102788 · Cited by 235 · see citing works

Towards Emotional Support Dialog Systems

Liu, S., Zheng, C., DeMasi, O., Sabour, S., Yu, L., Yu, Z., Jiang, Y., & Huang, M. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.269 · Cited by 286 · see citing works

Autoregressive Entity RetrievalPreprint

Cao, N. D., Izacard, G., Riedel, S., & Petroni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00904 · Cited by 307 · see citing works

Internet-Augmented Dialogue Generation

Komeili, M., Shuster, K., & Weston, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.579 · Cited by 192 · see citing works

Beyond Goldfish Memory: Long-Term Open-Domain Conversation

Xu, J., Szlam, A., & Weston, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.356 · Cited by 188 · see citing works

Untitled work

(2024) · doi:10.1145/3704435 · Cited by 184 · see citing works

Tool Learning with Foundation Models

Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.08354 · Cited by 184 · see citing works

Deep Learning for Text Style Transfer: A Survey

Jin, D., Jin, Z., Hu, Z., Olga Vechtomova, A., & Mihalcea, R. F. (2021) · Computational Linguistics · doi:10.1162/coli_a_00426 · Cited by 192 · see citing works

Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention

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

Aligning AI With Shared Human Values

Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., & Steinhardt, J. (2020) · arXiv · doi:10.48550/arxiv.2008.02275

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

Lin, Z., Madotto, A., Winata, G. I., & Fung, P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.273 · Cited by 158 · see citing works

Scaling Laws for Autoregressive Generative Modeling

Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.14701 · Cited by 150 · see citing works

Beyond Goldfish Memory: Long-Term Open-Domain Conversation

Xu, J., Szlam, A., & Weston, J. (2021) · arXiv · doi:10.48550/arxiv.2107.07567 · Cited by 148 · see citing works

Diffusion Model-Based Image Editing: A Survey

Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 167 · see citing works

Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization

Chintagunta, B., Katariya, N., Amatriain, X., & Kannan, A. (2021) · doi:10.18653/v1/2021.nlpmc-1.9 · Cited by 122 · see citing works

Concealed Data Poisoning Attacks on NLP Models

Wallace, E., Zhao, T., Feng, S., & Singh, S. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.13 · Cited by 115 · see citing works

Towards Persona-Based Empathetic Conversational Models

Zhong, P., Zhang, C., Wang, H., Liu, Y., & Miao, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.531 · Cited by 126 · see citing works

GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection

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Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., & Abend, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.619 · Cited by 113 · see citing works

Forecasting extreme labor displacement: A survey of AI practitioners

Gruetzemacher, R., Paradice, D., & Lee, K. B. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120323 · Cited by 105 · see citing works

MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models

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COLD: A Benchmark for Chinese Offensive Language Detection

Deng, J., Zhou, J., Sun, H., Zheng, C., Mi, F., Meng, H. M. L., & Huang, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.796 · Cited by 103 · see citing works

Pretrained Transformers for Text Ranking: BERT and BeyondPreprint

Lin, J., Nogueira, R. F., & Yates, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06467 · Cited by 101 · see citing works

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

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The Threat of Offensive AI to Organizations

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Investigating the user experience of customer service chatbot interaction: a framework for qualitative analysis of chatbot dialogues

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Recipes for Safety in Open-domain ChatbotsPreprint

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SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine TeachingPreprint

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S oloist : BuildingTask Bots at Scale with Transfer Learning and Machine Teaching

Peng, B., Li, C., Li, J., Shayandeh, S., Lidén, L., & Gao, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00399 · Cited by 264 · see citing works

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data

Song, H., Wang, Y., Zhang, K., Zhang, W., & Liu, T. (2021) · doi:10.18653/v1/2021.acl-long.14 · Cited by 95 · see citing works

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

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DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented DialoguePreprint

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Large Language Models in Mental Health Care: A Scoping Review

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Dialogue State Tracking with a Language Model using Schema-Driven Prompting

Lee, C., Cheng, H., & Mari Ostendorf, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.404 · Cited by 100 · see citing works

Conversational Information Seeking

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A Survey of Confidence Estimation and Calibration in Large Language Models . -

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MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation

Tu, Q., Li, Y., Cui, J., Wang, B., Wen, J.-R., & Yan, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.25 · Cited by 86 · see citing works

SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive ModelPreprint

Peng, B., Li, C., Li, J., Shayandeh, S., Lidén, L., & Gao, J. (2020) · arXiv (Cornell University) · Cited by 86 · see citing works

Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

Suresh, V., & Ong, D. C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.359 · Cited by 79 · see citing works

Knowledge Editing for Large Language Models: A Survey

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Information Retrieval

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What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

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GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

Huang, L., Ye, Z., Qin, J., Lin, L., & Liang, X. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.742 · Cited by 81 · see citing works

Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations

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Ensemble Large Language Models: A Survey

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“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset

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Dialogue Response Ranking Training with Large-Scale Human Feedback Data

Gao, X., Zhang, Y., Galley, M., Brockett, C., & Dolan, B. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.28 · Cited by 84 · see citing works

Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation

Peng, W., Hu, Y. H., Xing, L., Xie, Y., Sun, Y., & Li, Y. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/600 · Cited by 87 · see citing works

Between reality and delusion: challenges of applying large language models to companion robots for open-domain dialogues with older adults

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Dehallucinating Large Language Models Using Formal Methods Guided Iterative Prompting

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Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration

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Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion

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Reducing conversational agents' overconfidence through linguistic calibration

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Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities

Raamkumar, A. S., & Yang, Y. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3226693 · Cited by 75 · see citing works

DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances

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Contrastive Learning Reduces Hallucination in Conversations

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Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

Aliannejadi, M., Kiseleva, J., Chuklin, A., Dalton, J., & Burtsev, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.367 · Cited by 77 · see citing works

The CANDOR corpus: Insights from a large multimodal dataset of naturalistic conversation

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DWIE: An entity-centric dataset for multi-task document-level information extraction

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Bot-Adversarial Dialogue for Safe Conversational Agents

Xu, J., Ju, D., Li, M., Boureau, Y.-L., Weston, J., & Dinan, E. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.235 · Cited by 64 · see citing works

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Kim, H., Hessel, J., Jiang, L., West, P., Lu, X., Yu, Y., Zhou, P., Bras, R., Alikhani, M., Kim, G., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.799 · Cited by 63 · see citing works

FaithDial : A Faithful Benchmark for Information-Seeking Dialogue

Dziri, N., Kamalloo, E., Milton, S., Zai͏̈ane, O. R., Yu, M., Ponti, E. M., & Reddy, S. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00529 · Cited by 68 · see citing works

Self-Knowledge Guided Retrieval Augmentation for Large Language Models

Wang, Y., Li, P., Sun, M., & Liu, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.691 · Cited by 88 · see citing works

Detoxifying Language Models Risks Marginalizing Minority Voices

Xu, A., Pathak, E., Wallace, E., Gururangan, S., Sap, M., & Klein, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.190 · Cited by 81 · see citing works

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 · see citing works

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes

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Foundation Models for Decision Making: Problems, Methods, and Opportunities

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The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems

Ziems, C., Yu, J., Wang, Y., Halevy, A. Y., & Yang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.261 · Cited by 73 · see citing works

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 65 · see citing works

ChatGPT for Educational Purposes: Investigating the Impact of Knowledge Management Factors on Student Satisfaction and Continuous Usage

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Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration

Deng, Y., Liao, L., Chen, L., Wang, H., Lei, W., & Chua, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.711 · Cited by 55 · see citing works

Recursively summarizing enables long-term dialogue memory in large language models

Wang, Q., Fu, Y., Cao, Y., Wang, S., Tian, Z., & Ding, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130193 · Cited by 55 · see citing works

Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents

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Retrieval-Enhanced Machine Learning

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ChatGPT versus human essayists: An exploration of the impact of artificial intelligence for authorship and academic integrity in the humanities

Revell, T., Yeadon, W., Cahilly-Bretzin, G., Clarke, I., Manning, G., Jones, J., Mulley, C., Pascual, R. J., Bradley, N., Thomas, D., et al. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00161-8 · Cited by 51 · see citing works

Agentic AI: The age of reasoning—A review

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Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

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Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

Baheti, A., Sap, M., Ritter, A., & Riedl, M. O. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.397 · Cited by 52 · see citing works

Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation

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On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark

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AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation

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I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling

Nie, Y., Williamson, M., Bansal, M., Kiela, D., & Weston, J. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.134 · Cited by 58 · see citing works

DialFact: A Benchmark for Fact-Checking in Dialogue

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Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark

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Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts

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Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

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Deep Chit-Chat: Deep Learning for Chatbots

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PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

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Towards open domain spoken dialogue systems

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A Survey for Efficient Open Domain Question Answering

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