SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

The psychology of interpersonal relations.

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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LLaMA: Open and Efficient Foundation Language Models

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Training language models to follow instructions with human feedback

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The Power of Scale for Parameter-Efficient Prompt Tuning

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The Power of Scale for Parameter-Efficient Prompt Tuning

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Scaling Instruction-Finetuned Language Models

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Adafactor: Adaptive Learning Rates with Sublinear Memory CostPreprint

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The Function of Fiction is the Abstraction and Simulation of Social Experience

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Personalizing Dialogue Agents: I have a dog, do you have pets too?

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G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment

Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., & Zhu, C. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.153 · Cited by 948 · see citing works

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., & Zhu, C. (2023) · arXiv · doi:10.48550/arxiv.2303.16634 · Cited by 948 · see citing works

MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment

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Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset

Rashkin, H., Smith, E. M., Li, M., & Boureau, Y.-L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1534 · Cited by 883 · see citing works

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

GoEmotions: A Dataset of Fine-Grained Emotions

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Recipes for Building an Open-Domain Chatbot

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DailyDialog: A Manually Labelled Multi-turn Dialogue DatasetPreprint

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Multitask Prompted Training Enables Zero-Shot Task Generalization

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Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint

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Social IQa: Commonsense Reasoning about Social Interactions

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Data-Driven Response Generation in Social Media

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Scripts, plans, and knowledge

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Ethical and social risks of harm from Language Models

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Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogsPreprint

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What makes a good conversation? How controllable attributes affect human judgments

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Scaling Up Models and Data with t5x and seqioPreprint

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

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Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

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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

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.564 · Cited by 127 · see citing works

Modeling Naive Psychology of Characters in Simple Commonsense Stories

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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

(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs

Hwang, J. D., Bhagavatula, C., Bras, R. J. L., Da, J., Sakaguchi, K., Bosselut, A., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16792 · Cited by 53 · see citing works

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

AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models

Kulhánek, J., Hudeček, V., Nekvinda, T., & Dušek, O. (2021) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2021.nlp4convai-1.19 · Cited by 48 · see citing works

Weakly Supervised Data Augmentation Through Prompting for Dialogue UnderstandingPreprint

Chen, M., Papangelis, A., Tao, C., Rosenbaum, A., Kim, S., Liu, Y., Yu, Z., & Hakkani-Tür, D. Z. (2022) · arXiv.org · doi:10.48550/arxiv.2210.14169 · Cited by 42 · see citing works

System Error: Where Big Tech Went Wrong and How We Can Reboot

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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 · see citing works

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 38 · see citing works

GODEL: Large-Scale Pre-Training for Goal-Directed DialogPreprint

Peng, B., Galley, M., He, P., Brockett, C., Lidén, L., Nouri, E., Zhou, Y., Dolan, B., & Gao, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.11309 · Cited by 34 · see citing works

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 31 · see citing works

Social Psychology and Human Nature

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

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Controllable Dialogue Simulation with In-context Learning

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Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and ChallengesPreprint

Mehri, S., Choi, J., D’Haro, L. F., Deriu, J., Eskénazi, M., Gašić, M., Georgila, K., Hakkani-Tür, D. Z., Li, Z., Rieser, V., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.10012 · Cited by 23 · see citing works

Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelsPreprint

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Revealing Persona Biases in Dialogue SystemsPreprint

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GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation

Chen, D., & Yu, Z. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.35 · Cited by 19 · see citing works

Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality

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Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

Ou, J., Zhang, J., Feng, Y., & Zhou, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.106 · Cited by 12 · see citing works

Conversation as a system of social interaction

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BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets

Kim, M., Kim, C., Song, Y. H., Hwang, S.-W., & Yeo, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.344 · Cited by 9 · see citing works

How to Ask for Donations? Learning User-Specific Persuasive Dialogue Policies through Online Interactions

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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Ding, N., Chen, Y., Xu, B., Qin, Y., Hu, S., Liu, Z., Sun, M., & Zhou, B. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.183 · Cited by 130 · see citing works

Evaluating Very Long-Term Conversational Memory of LLM Agents

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

Kim, S., Joo, S., Kim, D., Jang, J., Ye, S., Shin, J., & Seo, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.782 · Cited by 69 · see citing works

MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems

Mačina, J., Daheim, N., Chowdhury, S. P., Sinha, T., Kapur, M., Gurevych, I., & Sachan, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.372 · Cited by 44 · see citing works

Prompted LLMs as Chatbot Modules for Long Open-domain Conversation

Lee, G., Hartmann, V., Park, J., Papailiopoulos, D., & Lee, K. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.277 · Cited by 34 · see citing works

Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties

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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 31 · see citing works

IntentQA: Context-aware Video Intent Reasoning

Li, J., Wei, P., Han, W., & Fan, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01099 · Cited by 28 · see citing works

Transforming Data Annotation with AI Agents: A Review of Architectures, Reasoning, Applications, and Impact

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FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Kim, H., Sclar, M., Zhou, X., Bras, R., Kim, G., Choi, Y., & Sap, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.890 · Cited by 27 · see citing works

Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art

Liu, C. C., Gurevych, I., & Korhonen, A. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00760 · Cited by 51 · see citing works

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

Shin, J., Yoon, H. S., Lee, S., Park, S., Liu, Y., Choi, J. D., & Lee, S.-J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.734 · Cited by 22 · see citing works

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents

Chae, H., Song, Y., Ong, K., Kwon, T., Kim, M., Yu, Y., Lee, D., Kang, D., & Yeo, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.342 · Cited by 23 · see citing works

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.482 · Cited by 21 · see citing works

NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation

Li, O., Subramanian, M., Saakyan, A., CH-Wang, S., & Muresan, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.974 · Cited by 19 · see citing works

A generative AI-driven interactive listening assessment task

Runge, A., Attali, Y., LaFlair, G. T., Park, Y., & Church, J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1474019 · Cited by 19 · see citing works

Acquiring and modeling abstract commonsense knowledge via conceptualization

He, M., Fang, T., Wang, W., & Song, Y. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104149 · Cited by 19 · see citing works

A survey on learning with noisy labels in Natural Language Processing: How to train models with label noise

Zhang, H., Zhang, Y., Li, J., Liu, J., & Ji, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110157 · Cited by 14 · see citing works

Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning

Yu, X., Chen, M., & Yu, Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.439 · Cited by 21 · see citing works

Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations

Jang, J., Boo, M., & Kim, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.838 · Cited by 13 · see citing works

COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements

Zhou, X., Zhu, H., Yerukola, A., Davidson, T., Hwang, J. D., Swayamdipta, S., & Sap, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.392 · Cited by 17 · see citing works

ConPrompt: Pre-training a Language Model with Machine-Generated Data for Implicit Hate Speech Detection

Kim, Y., Park, S., Namgoong, Y., & Han, Y.-S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.731 · Cited by 11 · see citing works

Adaptive Gating in Mixture-of-Experts based Language Models

Li, J., Su, Q., Yang, Y., Jiang, Y., Wang, C., & Xu, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.217 · Cited by 18 · see citing works

Leveraging Large Language Models for Automated Dialogue Analysis

Finch, S. E., Paek, E. S., & Choi, J. D. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.20 · Cited by 12 · see citing works

KLR-KGC: Knowledge-Guided LLM Reasoning for Knowledge Graph Completion

Ji, S., Liu, L., Xi, J., Zhang, X., & Li, X. (2024) · Electronics · doi:10.3390/electronics13245037 · Cited by 10 · see citing works

Champagne: Learning Real-world Conversation from Large-Scale Web Videos

Han, S., Hessel, J., Dziri, N., Choi, Y., & Yu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01421 · Cited by 14 · see citing works

The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trends

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Simultaneous text and gesture generation for social robots with small language models

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Complex QA and language models hybrid architectures, SurveyPreprint

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INQUIRER: Harnessing internal knowledge graphs for video question generation

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Dialogue agents 101: a beginner’s guide to critical ingredients for designing effective conversational systems

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Cultural Commonsense Knowledge for Intercultural Dialogues

Nguyen, T.-P., Razniewski, S., & Weikum, G. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679768 · Cited by 4 · see citing works

Evaluating Very Long-Term Conversational Memory of LLM Agents

Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · arXiv · doi:10.48550/arxiv.2402.17753 · Cited by 3 · see citing works

COBIAS: Assessing the Contextual Reliability of Bias Benchmarks for Language Models

Govil, P., Jain, H., Bonagiri, V. K., Chadha, A. R., Kumaraguru, P., Gaur, M., & Dey, S. (2025) · ACM Web Science Conference (WebSci) · doi:10.1145/3717867.3717923 · Cited by 3 · see citing works

Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts

Deng, J., Zhang, W., Jiao, Z., & Ren, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791186 · Cited by 3 · see citing works

Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs

Gody, R., Abdelghaffar, M., Jabreel, M., & Tawfik, A. Y. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00042 · Cited by 3 · see citing works

What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations

Rao, K., Jiang, L., Pyatkin, V., Gu, Y., Tandon, N., Dziri, N., Brahman, F., & Choi, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.812 · Cited by 3 · see citing works

NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation

West, P., Bras, R. J. L., Sorensen, T., Lin, B. Y., Jiang, L., Lu, X., Chandu, K. R., Hessel, J., Baheti, A., Bhagavatula, C., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.80 · Cited by 3 · see citing works

Enhancing Character-Coherent Role-Playing Dialogue with a Verifiable Emotion Reward

Wang, J., Wu, K., & Ouyang, Y. (2025) · Information · doi:10.3390/info16090738 · Cited by 2 · see citing works

A cognitive-affective chain-driven framework for emotion understanding

Chen, S., Liu, Z., Zhang, Z., Qin, K., Qian, Y., & Ma, F. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104367 · Cited by 2 · see citing works

COMET-M: Reasoning about Multiple Events in Complex Sentences

Ravi, S., Ng, R. T., & Shwartz, V. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.861 · Cited by 2 · see citing works

ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI

Finch, S. E., & Choi, J. D. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00659 · Cited by 2 · see citing works

AudioBERT: Audio Knowledge Augmented Language Model

Ok, H., Yoo, S., & Lee, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888629 · Cited by 2 · see citing works

Dialogue Generation Conditional on Predefined Stories: Preliminary Results

Miyazaki, C. (2023) · IEEE Access · doi:10.1109/access.2023.3303440 · Cited by 2 · see citing works

STEER: Unified Style Transfer with Expert Reinforcement

Hallinan, S., Brahman, F., Lu, X., Jung, J., Welleck, S., & Choi, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.506 · Cited by 3 · see citing works

Acquiring and modelling abstract commonsense knowledge via conceptualization

He, M. (2022) · doi:10.14711/thesis-991013098357503412 · Cited by 2 · see citing works

Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems

Vanel, L., Vela, A. R. R., Yacoubi, A., & Clavel, C. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00057 · Cited by 1 · see citing works

EmpLLM: Enhancing Empathy in LLMs Through Psychologist Simulation

Wang, Y., Zhang, H., & Shang, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-5123-8_14 · Cited by 1 · see citing works

Learning From Free-Text Human Feedback – Collect New Datasets Or Extend Existing Ones?

Petrak, D., Moosavi, N. S., Tian, Y., Rozanov, N., & Gurevych, I. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1011 · Cited by 2 · see citing works

Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms

Han, S., Kim, J., Hessel, J., Jiang, L., Chung, J., Son, Y., Choi, Y., & Yu, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.57 · Cited by 3 · see citing works

Analysis of medical conversations for the detection of depression

Zaman, I.-R., Trăușan-Matu, Ș., & Rebedea, T. E. (2023) · RoCHI - International Conference on Human-Computer Interaction (RoCHI) · doi:10.37789/rochi.2023.1.1.4 · Cited by 1 · see citing works

CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs

Aksu, T., Hazarika, D., Mehri, S., Kim, S., Hakkani-Tür, D. Z., Liu, Y., & Namazifar, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.717

Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

(2023) · doi:10.18653/v1/2023.pandl-1

Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations

Reddy, R. G., Bai, H., Yao, W., Suresh, S. C. E., Ji, H., & Zhai, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.62

Controlled Data Augmentation for Training Task-Oriented Dialog Systems with Low Resource Data

Steindl, S., Schäfer, U., & Ludwig, B. (2023) · doi:10.18653/v1/2023.pandl-1.9

Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments

Allein, L., Truşcǎ, M. M., & Moens, M. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104234

Utilizing Large Language Models for Customized Dialogue Data Augmentation and Psychological Counseling

Qi, Z. (2024) · Young Researchers' Roundtable on Spoken Dialogue Systems (YRRSDS) · doi:10.18653/v1/2024.yrrsds-1.31

CCDE: A Compact and Competitive Dialogue Evaluation Framework via Knowledge Distillation of Large Language Models

Ye, G., Zhao, H., Li, B., Chen, H., Zhao, Z., Jiang, Z., & Li, K. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3580272

Adapting Spoken Dialogue Systems to Low-Resource User Groups through Data Augmentation with a Focus on Dialogue Act Flow and Style

Qi, Z., & Inaba, M. (2025) · Journal of Natural Language Processing · doi:10.5715/jnlp.32.1030

Accelerating Personalization Signal Learning via Synthetic Data

Parveen, D., Kang, D., Paruchuri, A., Kayal, D., & Mallapragada, P. K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21321-1_17

Who’s Related? Fast and Accurate Family Relationship Detection in Conversations

Kushwaha, A., Sanchi, C., & Rajeshbhai, G. B. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463817

IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning

Li, J. H., Wei, P., Han, W., Zhu, S. C., & Fan, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3690561

Toward Exploring Mixed-Initiative Conversation Generation Based on Community Question Answering

Lu, L., Kasela, P., Ravenda, F., Meng, C., Pasi, G., & Crestani, F. (2026) · ACM Transactions on Information Systems · doi:10.1145/3814610

Can Large Language Models Really Recognize Your Name?

Pham, D., Kairouz, P., Mireshghallah, F., Bagdasarian, E., Pham, C. M., & Houmansadr, A. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806747

Arena: A Cognitive Multi-agent Framework for Modeling Conflict-Driven Multi-party Conversation

Sun, X., Zuo, L., Zeng, Z., Ngo, T. H., Liu, K., Lechervy, A., Jurie, F., Hatin, J., & Bertin, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37682-4_17

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents

Chae, H., Song, Y., Ong, K., Kwon, T., Kim, M., Yu, Y., Lee, D., Kang, D., & Yeo, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.342 · Cited by 23 · see citing works

Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot

Walker, M. A., Harrison, V., Juraska, J., Reed, L., Bowden, K. K., Cui, W. Q., Patil, O. R., & Ratnaparkhi, A. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.15 · Cited by 4 · see citing works

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Huang, L., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1243 · Cited by 278 · see citing works

DiffuCOMET: Contextual Commonsense Knowledge Diffusion

Gao, S., Ismayilzada, M., Zhao, M., Wakaki, H., Mitsufuji, Y., & Bosselut, A. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.264 · Cited by 16 · see citing works

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

West, P., Bhagavatula, C., Hessel, J., Hwang, J. D., Jiang, L., Bras, R. J. L., Lu, X., Welleck, S., & Choi, Y. (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.341 · Cited by 165 · see citing works