Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Open accessAbstract
Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and generation capabilities.However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context.Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user's hidden needs and respond satisfactorily through a single-step inference.To this end, we propose a novel linguistic cue-based chain-of-thoughts (Cue-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response.To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: personality, emotion, and psychology.We conduct extensive experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings.Empirical results demonstrate our proposed Cue-CoT method outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Dialogue CoT was introduced to assess in-depth dialogue, incorporating Explicit CoT and CoT.
“To effectively assess in-depth dialogue, Wang et al. [205] introduced the Dialogue CoT, incorporating two efficient dialogue strategies: Explicit CoT and CoT.”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [205]
The contributions passage (page not available in our copy) says the paper proposes two dialogue CoT approaches and builds a benchmark for evaluating in-depth dialogue responses. But it names the approaches O-Cue CoT and M-Cue CoT, not Explicit CoT and CoT. Section 3.1 likewise lists O-Cue CoT and M-Cue CoT as the prompting schemes. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774 · see citing works
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Universals and cultural differences in facial expressions of emotion.
Ekman, P. (1972) · Medical Entomology and Zoology · Cited by 2,273 · see citing works
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700 · see citing works
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of ReviewsPreprint
Turney, P. D. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0212032 · Cited by 1,584 · see citing works
Lying Words: Predicting Deception from Linguistic Styles
Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003) · Personality and Social Psychology Bulletin · doi:10.1177/0146167203029005010 · Cited by 1,441 · see citing works
Emergent Abilities of Large Language Models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · 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
Using linguistic cues for the automatic recognition of personality in conversation and text
Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2349 · Cited by 1,046 · see citing works
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., & Tang, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.26 · Cited by 1,028 · see citing works
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
Rashkin, H., Smith, E. M., Li, M., & Boureau, Y.-L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1534 · Cited by 883 · see citing works
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2203.11171 · Cited by 829 · see citing works
Complexity-Based Prompting for Multi-Step ReasoningPreprint
Fu, Y., Peng, H.-C., Sabharwal, A., Clark, P., & Khot, T. (2022) · International Conference on Learning Representations · doi:10.48550/arxiv.2210.00720 · Cited by 673 · see citing works
Is ChatGPT a Good NLG Evaluator? A Preliminary StudyPreprint
Wang, J., Liang, Y.-L., Meng, F.-D., Sun, Z., Shi, H., Li, Z.-X., Xu, J.-A., Qu, J.-F., & Zhou, J. (2023) · NEWSUM · doi:10.48550/arxiv.2303.04048 · Cited by 669 · see citing works
What Makes Good In-Context Examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022) · Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures · doi:10.18653/v1/2022.deelio-1.10 · Cited by 563 · see citing works
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
Ghosal, D., Majumder, N., Gelbukh, A. F., Mihalcea, R. F., & Poria, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.224 · Cited by 405 · see citing works
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., Lee, R. K.-W., & Lim, E.-P. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.147 · Cited by 344 · see citing works
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.04023 · Cited by 337 · cited 2× · see citing works
“, 2023), especially after the appearance of ChatGPT1, recently revolutionize the paradigm of various natural language processing (NLP) tasks, including dialogue response generation tasks (Bang et al., 2023).”
BackgroundLanguage Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022) · arXiv · doi:10.48550/arxiv.2201.07207 · Cited by 304 · 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
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
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
Automatic Chain of Thought Prompting in Large Language Models
Zhang, Z., Zhang, A., Li, M., & Smola, A. (2022) · arXiv · doi:10.48550/arxiv.2210.03493 · Cited by 274 · see citing works
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.425 · Cited by 268 · see citing works
Is chatGPT a good nlg evaluator? A preliminary study
Wang, J., Liang, Y., Meng, F., Sun, Z., Shi, H., Li, Z., Xu, J., Qu, J., & Zhou, J. (2023) · Proceedings of the 4th New Frontiers in Summarization Workshop · doi:10.18653/v1/2023.newsum-1.1 · Cited by 235 · see citing works
STaR: Bootstrapping Reasoning With Reasoning
Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022) · arXiv · doi:10.48550/arxiv.2203.14465 · Cited by 182 · see citing works
(2022) · doi:10.52202/068431-1126 · Cited by 182 · 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
CEM: Commonsense-Aware Empathetic Response Generation
Sabour, S., Zheng, C., & Huang, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21373 · Cited by 158 · see citing works
A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data
Zheng, Y., Zhang, R., Huang, M., & Mao, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6518 · Cited by 148 · see citing works
Selection-Inference: Exploiting Large Language Models for Interpretable Logical ReasoningPreprint
Creswell, A., Shanahan, M., & Higgins, I. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.09712 · Cited by 122 · see citing works
LaMP: When Large Language Models Meet Personalization
Salemi, A., Mysore, S., Bendersky, M., & Zamani, H. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.399 · Cited by 109 · see citing works
LaMP: When Large Language Models Meet Personalization
Salemi, A., Mysore, S., Bendersky, M., & Zamani, H. (2023) · arXiv · doi:10.48550/arxiv.2304.11406 · Cited by 109 · 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 105 · see citing works
Chen, Y., Wang, R., Jiang, H., Shi, S., & Xu, R. (2023) · doi:10.18653/v1/2023.findings-ijcnlp.32 · Cited by 93 · see citing works
Self-citationIs ChatGPT Equipped with Emotional Dialogue Capabilities?
Zhao, W., Zhao, Y., Lu, X., Wang, S., Tong, Y., & Qin, B. (2023) · arXiv · doi:10.48550/arxiv.2304.09582 · Cited by 92 · see citing works
PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support
Sun, H., Lin, Z., Zheng, C., Liu, S., & Huang, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.130 · Cited by 78 · see citing works
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 77 · see citing works
Self-citationA Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Deng, Y., Zhang, W., Xu, W., Lei, W., Chua, T., & Lam, W. P. (2022) · ACM Transactions on Information Systems · doi:10.1145/3570640 · Cited by 72 · see citing works
Self-citationCoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
Zheng, C., Liu, Y., Chen, W., Leng, Y., & Huang, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.72 · Cited by 70 · see citing works
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
Shum, K., Diao, S., & Zhang, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.811 · Cited by 65 · see citing works
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 49 · see citing works
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 48 · see citing works
Self-citationActive Learning Principles for In-Context Learning with Large Language Models
Margatina, K., Schick, T., Αλέτρας, Ν., & Dwivedi-Yu, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.334 · Cited by 42 · see citing works
Large Language Models are not Fair Evaluators
Wang, P., Li, L., Chen, L., Cai, Z., Zhu, D., Lin, B., Cao, Y., Liu, Q., Liu, T., & Sui, Z. (2023) · arXiv · doi:10.48550/arxiv.2305.17926 · Cited by 34 · 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
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 · see citing works
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat
Yao, B., Shi, C. J., Zou, L., Dai, L., Wu, M., Chen, L., Wang, Z., & Yu, K. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.156 · Cited by 30 · see citing works
Self-citationControllable Mixed-Initiative Dialogue Generation through Prompting
Chen, M., Yu, X., Shi, W., Awasthi, U., & Zhou, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.82 · Cited by 23 · see citing works
Ji, Y., Deng, Y., Gong, Y., Peng, Y., Niu, Q., Lei Zhang, A., Ma, B., & Li, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.14742 · Cited by 21 · see citing works
Self-citationWASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories
Barrière, V., Tafreshi, S., Sedoc, J., & Alqahtani, S. (2022) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2022.wassa-1.20 · Cited by 15 · see citing works
Building Emotional Support Chatbots in the Era of LLMsPreprint
Zheng, Z., Liao, L., Deng, Y., & Nie, L. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.11584 · Cited by 15 · see citing works
Self-citationTPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona CollaborationPreprint
Wang, H., Wang, H.-M., Wang, L.-Z., Hu, M.-D., Wang, R., Xue, B.-Y., Lu, H., Mi, F., & Wong, K.-F. (2023) · arXiv.org · doi:10.48550/arxiv.2309.16090 · Cited by 15 · see citing works
Self-citationTowards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Chen, L., Wang, H., Deng, Y., Kwan, W. C., Wang, Z., & Wong, K. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.462 · Cited by 13 · see citing works
Self-citationTeam IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection
Ghosh, S. K., Maurya, D., Ekbal, A., & Bhattacharyya, P. (2022) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2022.wassa-1.26 · Cited by 12 · see citing works
Wang, H., Cui, M., Zhou, Z., Fung, G. P. C., & Wong, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05187 · Cited by 10 · see citing works
Self-citationIntention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue
Ma, Z., Li, J., Zhang, Z., Li, G., & Cheng, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.174 · Cited by 5 · see citing works
Deng, Y., Liao, L., Chen, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.13626 · Cited by 5 · see citing works
Self-citationBERT: 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
Chen, Y., Wang, R., Jiang, H., Shi, S., & Xu, R. (2023) · arXiv · doi:10.48550/arxiv.2304.00723
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works
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 77 · see citing works
Self-citationTowards Human-centered Proactive Conversational Agents
Deng, Y., Liao, L., Zheng, Z., Yang, G. H., & Chua, T. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657843 · Cited by 51 · see citing works
Self-citationJiao, J., Afroogh, S., Murali, A., Chen, K., Atkinson, D., & Dhurandhar, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-18489-7 · Cited by 23 · see citing works
Chain-of-Thought Improves Text Generation with Citations in Large Language Models
Ji, B., Liu, H., Du, M., & Ng, S.-K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29794 · Cited by 16 · see citing works
Exploring Interpretability in Deep Learning for Affective Computing: A Comprehensive Review
Zhang, X., Zhang, T., Sun, L., Zhao, J., & Jin, Q. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3723005 · Cited by 18 · see citing works
M3sum: A Novel Unsupervised Language-Guided Video Summarization
Wang, H., Zhou, B., Zhang, Z., Du, Y., Ho, D., & Wong, K. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447504 · Cited by 14 · see citing works
Self-citationJiang, P. (2025) · Journal of Engineering Design · doi:10.1080/09544828.2025.2574209 · Cited by 11 · see citing works
Abdelnabi, A. A. B., Soykan, B., Bhatti, D., & Rabadi, G. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3712298 · Cited by 12 · see citing works
Biases in GPT-3.5 Turbo model: a case study regarding gender and language
Assi, F. M., & Caseli, H. D. M. (2024) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2024.245358 · Cited by 9 · see citing works
Empowering Large Language Models: Tool Learning for Real-World Interaction
Wang, H., Qin, Y., Lin, Y., Pan, J. Z., & Wong, K. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3661381 · Cited by 7 · see citing works
Self-citationGöldi, A., Rietsche, R., & Ungar, L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713606 · Cited by 6 · see citing works
Sun, X., de Wit, J., Li, Z., Pei, J., Ali, A. E., & Bosch, J. A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757655 · Cited by 11 · see citing works
Specify What? Enhancing Neural Specification Synthesis by Symbolic Methods
Granberry, G., Ahrendt, W., & Johansson, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76554-4_19 · Cited by 6 · see citing works
Generative AI in Multimodal Cross-Lingual Dialogue System for Inclusive Communication Support
Nataraj, V., Liao, W.-H., Chang, Y., Chiang, C.-Y., Lin, C., Lin, Y.-A., & Day, M.-Y. (2024) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri62200.2024.00051 · Cited by 7 · see citing works
Cohen, Y., & Aperstein, Y. (2024) · Preprints.org · doi:10.20944/preprints202405.0720.v1 · Cited by 5 · see citing works
Document-Level Event Extraction with Definition-Driven ICLPreprint
Liu, Z., & Luo, Y. (2024) · Preprints.org · doi:10.20944/preprints202408.0899.v1 · Cited by 4 · see citing works
Understanding Online Discussion Experiences of Blind Screen Reader Users
Ferdous, M. J., Nayak, A. K., Prakash, Y., Venkatraman, N., Jayarathna, S., Lee, H.-N., & Ashok, V. G. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2589865 · Cited by 3 · see citing works
AdMISC: Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation
Jia, X., He, J., Zhang, Q., & Jin, J. (2024) · Electronics · doi:10.3390/electronics13081484 · Cited by 3 · see citing works
MENTALER: Toward Professional Mental Health Support with LLMs via Multi-Role Collaboration
Gu, Z., & Zhu, Q. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822643 · Cited by 3 · see citing works
How Does Memorization Impact LLMs' Social Reasoning? An Assessment using Seen and Unseen Queries
Amirizaniani, M., Sivachenko, M., Lavergne, A., Shah, C. S., & Mashhadi, A. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703576 · Cited by 4 · see citing works
Enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning
Cao, Y., Chen, Z., Bi, G., Feng, Y., Chen, M., Wan, F., Huang, M., & Yu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_14 · Cited by 2 · see citing works
TPE: Towards Better Compositional Reasoning over Cognitive Tools via Multi-persona Collaboration
Wang, H., Wang, H., Wang, L., Hu, M., Wang, R., Xue, B., Huang, Y., & Wong, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_22 · Cited by 2 · see citing works
Self-citationDong, W., Wang, W., Han, X., Huang, J., Li, L., & Huang, Y. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110789 · Cited by 2 · see citing works
Seeking Specifications: The Case for Neuro-Symbolic Specification Synthesis
Granberry, G., Ahrendt, W., & Johansson, M. (2026) · Formal Aspects of Computing · doi:10.1145/3785411 · Cited by 1 · see citing works
LLM-Based Empathetic Response Through Psychologist-Agent Debate
Wu, Y., Feng, S., Ming, W., Wang, D., & Zhang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7232-2_14 · Cited by 1 · see citing works
Multiple Personalities by Order: Can ChatGPT Simulate Personas for User-Centered Design?
Bakaev, M., Gorovaia, S., & Mitrofanova, O. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96177-9_1 · Cited by 1 · see citing works
Enhancing Emotional Support Conversation System via Integrating Mental State-Strategy Reasoning
Cao, Y., Yu, H., & Wan, F. (2024) · International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) · doi:10.1109/mlbdbi63974.2024.10823858
Wu, D., Yu, Z., & li, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_15
From Chain to Loop: Improving Reasoning Capability in Small Language Models via Loop-of-Thought
Ji, M., Song, K., Jin, R., Qiu, X., Zhao, H., & Yang, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_23
Xu, X., Feng, S., Wang, D., Zhang, Y., & Yang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-3830-0_14
Ueno, T., & Inoshita, K. (2025) · IEEE Conference on Pervasive and Intelligent Computing (PICom) · doi:10.1109/picom68402.2025.00011
Suemitsu, K., Ban, M., Mitsuno, S., Ishiguro, H., & Yoshikawa, Y. (2026) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.41-2_ids26-e
coTherapist: A Behavior-Aligned Small Language Model Framework to Support Mental Healthcare Experts
Adhikary, P. K., Rawat, R., & Chakraborty, T. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792988
An Empathetic Response Generation Framework Based on the Cognition-Emotion Chain-of-Thought
Wan, Y., Dong, Z., Li, Y., Zhou, H., Jiang, B., & Ju, S. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3694031
Enhancing Resume Information Extraction via Instruction Tuning for Large Language Models
Yao, C., Liu, S., & He, M. (2026) · International Conference on Big Data and Informatization Education (ICBDIE) · doi:10.1145/3806980.3806993
Harnessing the Potential of LLMs in Hospital Management: Insights Into Medical Data Inquiry
li, J., Zhang, Y., Zhao, J., He, S., & Li, D. (2026) · The International Journal of Health Planning and Management · doi:10.1002/hpm.70106 · Cited by 1 · see citing works
PoCE: Automated Proof-of-Concept Synthesis using Large Language Models for Robust Validation
Tithy, T. D., Rodoshi, L. H., Azahar, A. R., Abhidarshi, A., Faruk, T., Rifat, F. A., & Shezan, F. H. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832134
- Authors
- Wang, H., Wang, R., Mi, F., Deng, Y., Wang, Z., Liang, B., Xu, R., & Wong, K.-F.
- Affiliations
- Chinese University of Hong Kong (HK)
- Harbin Institute of Technology (CN)
- Huawei Technologies (China) (CN)
- National University of Singapore (SG)
- Corresponding author
- Wong, Kam-Fai
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Published
- 2023
- Pages
- 12047-12064
- OpenAlex
- W4389524308
- Cited by
- 38
- References
- 56
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 16 · 2023: 1
- FWCI
- 6.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62006062, 62176076, 2022B1212010005)
- Natural Science Foundation of Guangdong Province (2023A1515012922)
- Chinese University of Hong Kong (4055209)
- Key Technologies Research and Development Program
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- chain-of-thought prompting, large language models, linguistic cues, emotion cues, dialogue systems, personalized responses
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
