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

Adam: A Method for Stochastic Optimization

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

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

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 54,325

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

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 · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×

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

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

Longformer: The Long-Document Transformer

Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281

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 1,476

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

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

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

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. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972

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

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

Deep learning for AI

Bengio, Y., LeCun, Y., & Hinton, G. E. (2021) · Communications of the ACM · doi:10.1145/3448250 · Cited by 791

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

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 1,097

A Literature Survey of Recent Advances in Chatbots

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

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 435

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 378

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

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

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

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

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

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

Autoregressive Entity RetrievalPreprint

Cao, N. D., Izacard, G., Riedel, S., & Petroni, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00904 · Cited by 384

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

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

Untitled work

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

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

Deep Learning for Text Style Transfer: A Survey

Jin, D., Jin, Z., Hu, Z., Vechtomova, O., & Mihalcea, R. F. (2021) · Computational Linguistics · doi:10.1162/coli_a_00426 · Cited by 217

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

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 · Cited by 517

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 171

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

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

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 278

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 129

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

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 142

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

He, W., Dai, Y., Zheng, Y., Wu, Y., Cao, Z., Liu, D., Jiang, P., Yang, M., Huang, F., Si, L., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21320 · Cited by 121

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

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

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

Xu, P., Patwary, M. A., Shoeybi, M., Puri, R., Fung, P., Anandkumar, A., & Catanzaro, B. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.226 · Cited by 127

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 143

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

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Chen, Y., Fu, Q., Yuan, Y., Wen, Z., Fan, G., Liu, D., Zhang, D., Li, Z., & Xiao, Y. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614905 · Cited by 128

The Threat of Offensive AI to Organizations

Mirsky, Y., Demontis, A., Kotak, J., Shankar, R., Gelei, D., Yang, L., Zhang, X., Pintor, M., Lee, W., Elovici, Y., et al. (2023) · Computers & Security · doi:10.1016/j.cose.2022.103006 · Cited by 97

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

Følstad, A., & Taylor, C. (2021) · Quality and User Experience · doi:10.1007/s41233-021-00046-5 · Cited by 109

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 236

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

Peng, B., Li, C., Li, J., Shayandeh, S., Lidén, L., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05298 · Cited by 96

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 295

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 115

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

Bao, S., Huang, H., Wang, F., Wu, H. R., Wang, H., Wu, W., Guo, Z., Liu, Z., & Xu, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.222 · Cited by 118

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

Mehri, S., Eric, M., & Hakkani-Tür, D. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13570 · Cited by 141

Large Language Models in Mental Health Care: A Scoping Review

Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., et al. (2025) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-025-00363-y · Cited by 92

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 112

Conversational Information Seeking

Zamani, H., Trippas, J. R., Dalton, J., & Radlinski, F. (2023) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000081 · Cited by 178

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

Geng, J., Cai, F., Wang, Y., Koeppl, H., Nakov, P., & Gurevych, I. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.366 · Cited by 88

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

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

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 82

Knowledge Editing for Large Language Models: A Survey

Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., & Li, J. (2025) · ACM Computing Surveys · doi:10.1145/3698590 · Cited by 79

Information Retrieval

(2024) · ACM eBooks · doi:10.1145/3674127 · Cited by 86

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

Kim, B., Kim, H., Lee, S., Lee, G., Kwak, D., Hyeon, J. D., Park, S., Kim, S., Kim, S., Seo, D., et al. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.274 · Cited by 120

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 93

Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations

Hsu, J.-H., Su, M.-H., Wu, C., & Chen, Y. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3076364 · Cited by 78

Ensemble Large Language Models: A Survey

Mienye, I. D., & Swart, T. G. (2025) · Information · doi:10.3390/info16080688 · Cited by 100

“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

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

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 100

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

Irfan, B., Kuoppamäki, S., Hosseini, A., & Skantze, G. (2025) · Autonomous Robots · doi:10.1007/s10514-025-10190-y · Cited by 92

Dehallucinating Large Language Models Using Formal Methods Guided Iterative Prompting

Jha, S., Jha, S. K., Lincoln, P. D., Bastian, N. D., Velasquez, A., & Neema, S. K. (2023) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa58325.2023.00029 · Cited by 81

Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration

Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00494 · Cited by 72

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

Shuster, K., Komeili, M., Adolphs, L., Roller, S., Szlam, A. D., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.27 · Cited by 118

Reducing conversational agents' overconfidence through linguistic calibration

Mielke, S. J., Szlam, A., Dinan, E., & Boureau, Y.-L. (2020) · arXiv · doi:10.48550/arxiv.2012.14983 · Cited by 72

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

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

Gu, X., Yoo, K. M., & Ha, J.-W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17527 · Cited by 74

Contrastive Learning Reduces Hallucination in Conversations

Sun, W., Shi, Z., Gao, S., Ren, P., De Rijke, M., & Ren, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26596 · Cited by 69

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 93

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

Reece, A., Cooney, G., Bull, P. E., Chung, C., Dawson, B., Fitzpatrick, C. A., Glazer, T., Knox, D., Liebscher, A., & Marin, S. (2023) · Science Advances · doi:10.1126/sciadv.adf3197 · Cited by 114

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

Zaporojets, K., Deleu, J., Develder, C., & Demeester, T. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102563 · Cited by 77

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

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

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 86

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 141

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 131

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

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

Kim, H., Kim, B., & Kim, G. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.170 · Cited by 66

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

Yang, S., Nachum, O., Du, Y., Wei, J., Abbeel, P., & Schuurmans, D. (2023) · arXiv · doi:10.48550/arxiv.2303.04129 · Cited by 60

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 107

Generative Spoken Dialogue Language Modeling

Nguyen, T. A., Kharitonov, E., Copet, J., Adi, Y., Hsu, W.-N., Elkahky, A. M., Tomasello, P., Algayres, R., Sagot, B., Mohamed, A., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00545 · Cited by 97

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

Ngo, T. T. A., Tran, T. T., An, G. K., & Nguyen, P. T. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3383773 · Cited by 56

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.840 · Cited by 56

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 113

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

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

Young, T., Xing, F., Pandelea, V., Ni, J., & Cambria, E. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21416 · Cited by 57

Retrieval-Enhanced Machine Learning

Zamani, H., Díaz, F., Dehghani, M., Metzler, D., & Bendersky, M. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531722 · Cited by 57

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

Agentic AI: The age of reasoning—A review

Nisa, U., Shirazi, M., Saip, M. A., & Pozi, M. S. M. (2025) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2025.08.003 · Cited by 84

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

Kim, H., Kim, B., & Kim, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.65 · Cited by 55

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 60

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

Li, C., Xing, W., & Leite, W. L. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13227 · Cited by 59

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

Sun, H., Xu, G., Deng, J., Cheng, J., Zheng, C., Zhou, H., Peng, N., Zhu, X., & Huang, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.308 · Cited by 71

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

Zheng, C., Sabour, S., Wen, J., Zhang, Z., & Huang, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.99 · Cited by 84

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 66

DialFact: A Benchmark for Fact-Checking in Dialogue

Gupta, P., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.263 · Cited by 48

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

Bae, S., Kwak, D., Kim, S., Ham, D., Kang, S., Lee, S., & Park, W. (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.155 · Cited by 31

Deep Chit-Chat: Deep Learning for Chatbots

Wu, W., & Yan, R. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3320084 · Cited by 7

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

Bao, S., Huang, H., Wang, F., Wu, H. R., Wang, H., Wu, W., Guo, Z., Liu, Z., & Xu, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.222 · Cited by 118

Towards open domain spoken dialogue systems

Young, S. K. (2016) · Cambridge University Engineering Department Publications Database

A Survey for Efficient Open Domain Question Answering

Zhang, Q., Chen, S., Xu, D., Cao, Q., Chen, X., Cohn, T., & Fang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.808 · Cited by 54