Papers by Xiaoxi Kang
NAP2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human (2025.findings-emnlp)
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Shuo Huang, William Maclean, Xiaoxi Kang, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu
| Challenge: | a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer . |
| Approach: | They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models . |
| Outcome: | The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool . |
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer? (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have attracted a lot of attention in the legal domain due to their ability to tackle a variety of legal tasks. |
| Approach: | They constructed a corpus consisting of two legal scenarios using the IRAC method and used it to perform analysis on the corpus. |
| Outcome: | The proposed model can analyze a contract act in Malaysia and the Australian Social Act for Dependent Child using the IRAC method. |
On the Reliability of Large Language Models for Causal Discovery (2025.acl-long)
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| Challenge: | Existing statistical methods to identify causal relationships from observational data remain elusive. |
| Approach: | They examine the impact of memorization for accurate causal relation prediction, the influence of incorrect causal relations in pre-training data and the contextual nuances that influence LLMs’ understanding of causal relations. |
| Outcome: | The proposed models are effective in recognizing causal relations that occur frequently in pre-training data, but their ability to generalize to new or rare causal relations is limited. |
CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems (2025.coling-main)
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| Challenge: | Existing metrics for dialogue quality evaluation show low correlation with human judgements . current metrics do not accurately evaluate dialogue responses based on dialogue history . |
| Approach: | They propose a new metric measuring causal strength between dialogue histories and responses . they collect a dialogue dataset with human-annotated causal relations and pairwise human judgements . |
| Outcome: | The proposed metric outperforms existing state-of-the-art metrics in human judgements . it is based on a dialogue dataset with human-annotated causal relations and human judgement sets . |
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
| Challenge: | Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. |
| Approach: | They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability. |
| Outcome: | The proposed system can understand and remediate norm violations step by step. |
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning (2025.naacl-long)
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Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari
| Challenge: | Existing methods for identifying event causality in NLP are limited in their scale and rely on lexical cues. |
| Approach: | They propose a benchmark for identifying abstract causality from a large-scale dataset. |
| Outcome: | The proposed benchmark can be leveraged for enhancing QA reasoning performance in LLMs. |
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues (2026.acl-long)
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Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava, Chetan Arora, Adnan Trakic, Lay-Ki Soon, Md Khalid Hossain, Lizhen Qu
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities, but their precision remains inadequate. |
| Approach: | They propose a framework that integrates neural generation with statistical reasoning to improve the accuracy of large language models. |
| Outcome: | The proposed framework achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. |