Research Interests

Natural Language Processing
Large Language Models
Theory of Mind
Computational Linguistics and Discourse Analysis

Awards

Hong Kong PhD Fellowship (2023 - 2026)
HKUST RedBird PhD Scholarship (2023)
ICMLC 2023 Best Student Paper Awards (2023)
Hong Kong Talent Development Scholarship for Msc Programs in the School of Engineering (2020-21 Fall, Winter, Spring)
Dean’s List for the School of Engineering (2019/20)

Publications

EMNLP 2024

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding

Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, and Yangqiu Song

arXiv preprint arXiv:2404.13627, 2024
Arxiv

Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information

Yauwai Yim, Chunkit Chan, Tianyu Shi, Zheye Deng, Wei Fan, Tianshi Zheng, and Yangqiu Song

CoRR, 2024
Arxiv

Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models

Zizheng Lin, Chunkit Chan, Yangqiu Song, and Xin Liu

arXiv preprint arXiv:2409.13490, 2024
EACL 2024

ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations

Chunkit Chan, Jiayang Cheng, Weiqi Wang, Yuxin Jiang, Tianqing Fang, Xin Liu, and Yangqiu Song

Findings-EACL2024, 2023

ACL 2023

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Chunkit Chan, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Y. Wong, and Simon See

In Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, 2023

Bib

@inproceedings{DBLP:conf/acl/ChanLCLSWS23,
  author = {Chan, Chunkit and Liu, Xin and Cheng, Jiayang and Li, Zihan and Song, Yangqiu and Wong, Ginny Y. and See, Simon},
  editor = {Rogers, Anna and Boyd{-}Graber, Jordan L. and Okazaki, Naoaki},
  title = {DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse
                    Relation Recognition},
  booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2023,
                    Toronto, Canada, July 9-14, 2023},
  pages = {35--57},
  publisher = {Association for Computational Linguistics},
  year = {2023},
  url = {https://doi.org/10.18653/v1/2023.findings-acl.4},
  doi = {10.18653/v1/2023.findings-acl.4},
  timestamp = {Thu, 10 Aug 2023 12:35:51 +0200},
  biburl = {https://dblp.org/rec/conf/acl/ChanLCLSWS23.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

AACL 2023

Self-Consistent Narrative Prompts on Abductive Natural Language Inference

Chunkit Chan, Xin Liu, Tsz Ho Chan, Jiayang Cheng, Yangqiu Song, Ginny Wong, and Simon See

In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), Nov 2023

Bib

@inproceedings{chan-etal-2023-self,
  title = {Self-Consistent Narrative Prompts on Abductive Natural Language Inference},
  author = {Chan, Chunkit and Liu, Xin and Chan, Tsz Ho and Cheng, Jiayang and Song, Yangqiu and Wong, Ginny and See, Simon},
  editor = {Park, Jong C. and Arase, Yuki and Hu, Baotian and Lu, Wei and Wijaya, Derry and Purwarianti, Ayu and Krisnadhi, Adila Alfa},
  booktitle = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month = nov,
  year = {2023},
  address = {Nusa Dua, Bali},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2023.ijcnlp-main.67},
  pages = {1040--1057}
}

EMNLP 2023
Lion: Adversarial Distillation of Proprietary Large Language Models

Yuxin Jiang, Chunkit Chan, Mingyang Chen, and Wei Wang

In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023

Abs Bib

The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention. Previous works have focused on a unidirectional knowledge distillation way by aligning the responses of the student model with those of the teacher model to a set of instructions. Nevertheless, they overlooked the possibility of incorporating any “feedback”–identifying challenging instructions where the student model’s performance falls short–to boost the student model’s proficiency iteratively. To this end, we propose a novel adversarial distillation framework for a more efficient knowledge transfer. Leveraging the versatile role adaptability of LLMs, we prompt the teacher model to identify “hard” instructions and generate new “hard” instructions for the student model, creating a three-stage adversarial loop of imitation, discrimination, and generation. By applying this adversarial framework, we successfully transfer knowledge from ChatGPT to a student model (named Lion), using a mere 70k training data. Our results show that Lion-13B not only achieves comparable open-ended generation capabilities to ChatGPT but surpasses conventional state-of-the-art (SOTA) instruction-tuned models like Vicuna-13B by 55.4% in challenging zero-shot reasoning benchmarks such as BIG-Bench Hard (BBH) and 16.7% on AGIEval.
@inproceedings{jiang-etal-2023-lion, title = {Lion: Adversarial Distillation of Proprietary Large Language Models}, author = {Jiang, Yuxin and Chan, Chunkit and Chen, Mingyang and Wang, Wei}, editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika}, booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing}, month = dec, year = {2023}, address = {Singapore}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.emnlp-main.189}, doi = {10.18653/v1/2023.emnlp-main.189}, pages = {3134--3154} }
EMNLP 2024

Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

Zheye Deng, Chunkit Chan, Weiqi Wang, Yuxi Sun, Wei Fan, Tianshi Zheng, Yauwai Yim, and Yangqiu Song

arXiv preprint arXiv:2404.14215, Dec 2024

COLING 2024

EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs

Cheng Jiayang, Lin Qiu, Chunkit Chan, Xin Liu, Yangqiu Song, and Zheng Zhang

In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy, Dec 2024

Bib

@inproceedings{DBLP:conf/coling/JiayangQC0SZ24,
  author = {Jiayang, Cheng and Qiu, Lin and Chan, Chunkit and Liu, Xin and Song, Yangqiu and Zhang, Zheng},
  editor = {Calzolari, Nicoletta and Kan, Min{-}Yen and Hoste, V{\'{e}}ronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
  title = {EventGround: Narrative Reasoning by Grounding to Eventuality-centric
                    Knowledge Graphs},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational
                    Linguistics, Language Resources and Evaluation, {LREC/COLING} 2024,
                    20-25 May, 2024, Torino, Italy},
  pages = {6622--6642},
  publisher = {{ELRA} and {ICCL}},
  year = {2024},
  url = {https://aclanthology.org/2024.lrec-main.587},
  timestamp = {Thu, 23 May 2024 16:47:05 +0200},
  biburl = {https://dblp.org/rec/conf/coling/JiayangQC0SZ24.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}