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

Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark

Dziri, N., Rashkin, H., Linzen, T., & Reitter, D. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00506 · Cited by 79

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

Hake, J., Crowley, M., Coy, A., Shanks, D., Eoff, A., Kirmer-Voss, K., Dhanda, G., & Parente, D. J. (2024) · The Annals of Family Medicine · doi:10.1370/afm.3075 · Cited by 61

Large Language Models in Mental Health Care: A Systematic Scoping Review (Preprint)

hua, Y., Liu, F., Yang, K., Li, Z., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., & Beam, A. (2024) · doi:10.2196/preprints.64088 · Cited by 46

Learning towards conversational AI: A survey

Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46

Plug-and-Play Conversational Models

Madotto, A., Ishii, E., Lin, Z., Dathathri, S., & Fung, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.219 · Cited by 51

Learning Neural Templates for Recommender Dialogue System

Liang, Z., Hu, H., Xu, C., Miao, J., He, Y., Chen, Y., Geng, X., Liang, F., & Jiang, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.617 · Cited by 46

Understanding and predicting user dissatisfaction in a neural generative chatbot

See, A., & Manning, C. D. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.1 · Cited by 44

Conversational AI: An Explication of Few-Shot Learning Problem in Transformers-Based Chatbot Systems

Ahmed, M., Khan, H. U., & Munir, E. U. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3281492 · Cited by 44

Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements

Qian, Y., Zhang, W., & Liu, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.433 · Cited by 73

Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain

Meloni, A., Angioni, S., Salatino, A. A., Osborne, F., Recupero, D. R., & Motta, E. (2023) · IEEE Access · doi:10.1109/access.2023.3253388 · Cited by 50

Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

He, W., Dai, Y., Yang, M., Sun, J. Q., Huang, F., Si, L., & Li, Y. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532069 · Cited by 44

Chain-of-Verification Reduces Hallucination in Large Language Models

Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023) · arXiv · doi:10.48550/arxiv.2309.11495 · Cited by 42

FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation

Peng, W., Qin, Z., Hu, Y., Xie, Y., & Li, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110340 · Cited by 43

Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities

Deng, Y., Liao, L., Lei, W., Yang, G. H., Lam, W. P., & Chua, T. (2025) · ACM Transactions on Information Systems · doi:10.1145/3715097 · Cited by 67

The Adapter-Bot: All-In-One Controllable Conversational Model

Lin, Z., Madotto, A., Bang, Y., & Fung, P. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.18018 · Cited by 42

Adding Chit-Chat to Enhance Task-Oriented Dialogues

Sun, K., Moon, S., Crook, P., Roller, S., Silvert, B., Liu, B., Wang, Z., Liu, H., Cho, E., & Cardie, C. (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.124 · Cited by 55

Domain-matched Pre-training Tasks for Dense Retrieval

Oğuz, B., Lakhotia, K., Gupta, A., Lewis, P., Karpukhin, V., Piktus, A., Chen, X., Riedel, S., Yih, S., Gupta, S., et al. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.114 · Cited by 50

A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation

Cao, Y., Bi, W., Fang, M., Shi, S., & Tao, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.550 · Cited by 45

Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents

Smith, E., Hsu, O., Qian, R., Roller, S., Boureau, Y.-L., & Weston, J. (2022) · Proceedings of the 4th Workshop on NLP for Conversational AI · doi:10.18653/v1/2022.nlp4convai-1.8 · Cited by 40

When interacting with a service robot is (not) satisfying: The role of customers’ need for social sharing of emotion

Lajante, M., Tojib, D., & Ho, T. H. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107792 · Cited by 45

Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations

Deng, Y., Zhang, W., Yuan, Y., & Lam, W. P. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.225 · Cited by 66

Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education

Parente, D. J. (2024) · Family Medicine · doi:10.22454/fammed.2024.775525 · Cited by 40

ToD4IR: A Humanised Task-Oriented Dialogue System for Industrial Robots

Li, C., Zhang, X., Chrysostomou, D., & Yang, H. (2022) · IEEE Access · doi:10.1109/access.2022.3202554 · Cited by 40

Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation

Li, Y., Peng, B., Shen, Y., Mao, Y., Lidén, L., Yu, Z., & Gao, J. (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.15 · Cited by 46

Empirical Analysis of Training Strategies of Transformer-Based Japanese Chit-Chat Systems

Sugiyama, H., Mizukami, M., Arimoto, T., Narimatsu, H., Chiba, Y., Nakajima, H., & Meguro, T. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022973 · Cited by 39

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

Commonsense-Focused Dialogues for Response Generation: An Empirical Study

Zhou, P., Gopalakrishnan, K., Hedayatnia, B., Kim, S., Pujara, J., Ren, X., Liu, Y., & Hakkani-Tür, D. Z. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.13 · Cited by 40

Why So Toxic?

Si, W. M., Michael Backes, A., Blackburn, J., Cristofaro, E. D., Stringhini, G., Zannettou, S., & Zhang, Y. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560599 · Cited by 52

Towards a comprehensive understanding of the impact of large language models on natural language processing: challenges, opportunities and future directions^†

Huang, X., Liu, Z. Y., Zhang, M., 桂, 韬., 韩, 先., 户, 保., 黄, 民., 黄, 萱., 刘, 康., 刘, 挺., et al. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0113 · Cited by 42

“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (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.60 · Cited by 48

RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.275 · Cited by 37

CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

Zhong, P., Wang, D., Li, P., Zhang, C., Wang, H., & Miao, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17713 · Cited by 38

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

Cheng, Y., Liu, W., Li, W., Wang, J., Zhao, R., Liu, B., Liang, X., & Zheng, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.195 · Cited by 64

Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents

Grassi, L., Recchiuto, C. T., & Sgorbissa, A. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00868-z · Cited by 42

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation

Zhou, J., Zheng, C., Wang, B., Zhang, Z., & Huang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.457 · Cited by 38

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 403

Multi-Modal Open-Domain Dialogue

Shuster, K., Smith, E. M., Ju, D., & Weston, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.398 · Cited by 44

NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation

Wang, X., Li, C., Zhao, J., & Yu, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17649 · Cited by 36

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

Gupta, P., Jiao, C., Yeh, Y.-T., Mehri, S., Eskénazi, M., & Bigham, J. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.33 · Cited by 75

Controlling Style in Generated DialoguePreprint

Smith, E. M., Gonzalez-Rico, D., Dinan, E., & Boureau, Y.-L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10855 · Cited by 53

SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems

Dinan, E., Abercrombie, G., Bergman, A., Spruit, S., Hovy, D., Boureau, Y.-L., & Rieser, V. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.284 · Cited by 32

Controlling Dialogue Generation with Semantic Exemplars

Gupta, P., Bigham, J. P., Tsvetkov, Y., & Pavel, A. (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.240 · Cited by 42

Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

Jang, Y., Lim, J., Hur, Y., Oh, D., Son, S., Lee, Y., Shin, D., Kim, S., & Lim, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21326 · Cited by 39

ConvXAI: a System for Multimodal Interaction with Any Black-box Explainer

Malandri, L., Mercorio, F., Mezzanzanica, M., & Nobani, N. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10067-7 · Cited by 39

PLACES: Prompting Language Models for Social Conversation Synthesis

Chen, M., Papangelis, A., Tao, C., Kim, S., Rosenbaum, A., Liu, Y., Zhou, Y., & Hakkani-Tür, D. Z. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.63 · Cited by 53

Can Language Models Employ the Socratic Method? Experiments with Code Debugging

Al-Hossami, E., Bunescu, R., Smith, J., & Teehan, R. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630799 · Cited by 46

Dialoging Resonance in Human-Chatbot Conversation: How Users Perceive and Reciprocate Recommendation Chatbot's Self-Disclosure Strategy

Liang, K.-H., Shi, W., Oh, Y. J., Wang, H., Zhang, J., & Yu, Z. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3653691 · Cited by 46

Multimodal Dialogue Response Generation

Sun, Q., Wang, Y., Xu, C., Zheng, K., Yang, Y., Hu, H., Xu, F., Zhang, J., Geng, X., & Jiang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.204 · Cited by 34

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Arora, K., Asri, L. E., Bahuleyan, H., & Cheung, J. C. K. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.58 · Cited by 100

Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems

Deriu, J., Tuggener, D., von Däniken, P., Campos, J. A., Rodrigo, Á., Belkacem, T., Soroa, A., Agirre, E., & Cieliebak, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.326 · Cited by 30

Defining Patterns for a Conversational Web

Pucci, E., Possaghi, I., Cutrupi, C. M., Báez, M., Cappiello, C., & Matera, M. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581145 · Cited by 29

Keep Me Updated! Memory Management in Long-term Conversations

Bae, S., Kwak, D., Kang, S., Lee, M. Y., Kim, S., Jeong, Y., Kim, H., Lee, S.-W., Park, W., & Sung, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.276 · Cited by 29

EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-TrainingPreprint

Zhou, H., Ke, P., Zhang, Z., Gu, Y., Zheng, Y., Zheng, C., Wang, Y., Wu, C., Sun, H., Yang, X., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01547 · Cited by 39

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

Xu, Y., Ishii, E., Cahyawijaya, S., Liu, Z., Winata, G. I., Madotto, A., Su, D., & Fung, P. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.10 · Cited by 29

Deep Learning Mental Health Dialogue System

Brocki, L., Dyer, G. C., Gładka, A., & Chung, N. C. (2023) · 2023 IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp57234.2023.00097 · Cited by 28

A3-CodGen: A Repository-Level Code Generation Framework for Code Reuse With Local-Aware, Global-Aware, and Third-Party-Library-Aware

Liao, D., Pan, S., Sun, X., Ren, X., Huang, Q., Xing, Z., Jin, H., & Li, Q. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3486195 · Cited by 23

How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

Ammanabrolu, P., Urbanek, J., Li, M., Szlam, A. D., Rocktäschel, T., & Weston, J. (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.64 · Cited by 35

PAL: Persona-Augmented Emotional Support Conversation Generation

Cheng, J., Sabour, S., Sun, H., Chen, Z., & Huang, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.34 · Cited by 32

Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues

Chen, R., Wang, J., Yu, L.-C., & Zhang, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26489 · Cited by 35

A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook

Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Qiu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2025) · ACM Computing Surveys · doi:10.1145/3729218 · Cited by 45

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

Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation

Zhou, P., Gopalakrishnan, K., Hedayatnia, B., Kim, S., Pujara, J., Ren, X., Liu, Y., & Hakkani-Tür, D. Z. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.88 · Cited by 26

SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Chiu, S., Li, M., Lin, Y.-T. R., & Chen, Y.-N. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.425 · Cited by 28

Personalized Dialogue Generation with Persona-Adaptive Attention

Huang, Q., Zhang, Y., Ko, T., Liu, X., Wu, B., Wang, W., & Tang, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26518 · Cited by 27

Unveiling the Implicit Toxicity in Large Language Models

Wen, J., Ke, P., Sun, H., Zhang, Z., Li, C., Bai, J., & Huang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.84 · Cited by 76

Reason first, then respond: Modular Generation for Knowledge-infused Dialogue

Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.527 · Cited by 25

Open-domain Dialogue Generation: What We Can Do, Cannot Do, And Should Do Next

Kann, K., Ebrahimi, A., Koh, J. J., Dudy, S., & Roncone, A. (2022) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2022.nlp4convai-1.13 · Cited by 28

Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach

Zhou, J., Chen, Z., Wang, B., & Huang, M. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.96 · Cited by 25

Opportunities and Challenges in Neural Dialog Tutoring

Mačina, J., Daheim, N., Wang, L., Sinha, T., Kapur, M., Gurevych, I., & Sachan, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.173 · Cited by 37

Introducing CounseLLMe: A dataset of simulated mental health dialogues for comparing LLMs like Haiku, LLaMAntino and ChatGPT against humans

Duro, E. S. D., Improta, R., & Stella, M. (2025) · Emerging Trends in Drugs Addictions and Health · doi:10.1016/j.etdah.2025.100170 · Cited by 28

Towards Multimodal Emotional Support Conversation Systems

Chu, Y., Liao, L., Zhou, Z., Ngo, C., & Hong, R. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604951 · Cited by 24

A Multi-Persona Chatbot for Hotline Counselor Training

DeMasi, O., Li, Y., & Yu, Z. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.324 · Cited by 27

Integrated taxonomy of errors in chat-oriented dialogue systems

Higashinaka, R., Araki, M., Tsukahara, H., & Mizukami, M. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.10 · Cited by 24

"I don't know how to help with that" - Learning from Limitations of Modern Conversational Agent Systems in Caregiving Networks

Zubatiy, T., Mathur, N., Heck, L. P., Vickers, K. L., Rozga, A., & Mynatt, E. D. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610170 · Cited by 24

STAR: A Schema-Guided Dialog Dataset for Transfer LearningPreprint

Mosig, J. E. M., Mehri, S., & Kober, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11853 · Cited by 56

Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations

Al-Hossami, E., Bunescu, R., Teehan, R., Powell, L., Mahajan, K., & Dorodchi, M. (2023) · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) · doi:10.18653/v1/2023.bea-1.57 · Cited by 23

Context-Aware Abbreviation Expansion Using Large Language Models

Cai, S., Venugopalan, S., Tomanek, K., Narayanan, A., Morris, M. R., & Brenner, M. P. (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.91 · Cited by 26

Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models

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Helping the Helper : Supporting Peer Counselors via AI-Empowered Practice and Feedback

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Recipient Design for Conversational Agents: Tailoring Agent’s Utterance to User’s Knowledge

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SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures

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Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems

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LLaVA-docent: Instruction tuning with multimodal large language model to support art appreciation education

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Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation

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How Robust are Fact Checking Systems on Colloquial Claims?

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Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances

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Item Response Theory for Efficient Human Evaluation of Chatbots

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Do-Not-Answer: Evaluating Safeguards in LLMs

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Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

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Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation

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Persona-Based Conversational AI: State of the Art and Challenges

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Controllable Mixed-Initiative Dialogue Generation through Prompting

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Diving Deep into Modes of Fact Hallucinations in Dialogue Systems

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Maria: A Visual Experience Powered Conversational Agent

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Q-TOD: A Query-driven Task-oriented Dialogue System

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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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