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6 Papers Accepted at ACL 2025
04 Jul 2025
The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) will take place in Vienna, Austria from 27 July – 1 August 2025. This premier annual conference highlights cutting-edge research in computational linguistics and natural language processing (NLP).
Congratulations to the following scientists from A*STAR Centre for Frontier AI Research (A*STAR CFAR) on having their papers accepted:
- Dr Joey Zhou, Deputy Director
- Dr Cheston Tan, Senior Principal Scientist
- Dr Foo Chuan Sheng, Principal Scientist
- Dr Basura Fernando, Principal Scientist
List of accepted papers:
| 1. | Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low This paper proposes a WAtermarking for Source Attribution (WASA) framework, the first of its kind designed to enable large language models (LLMs) to generate texts for effective source attribution. Note: This paper was accepted to the Findings Track. |
| 2. | Jiaxu Zhao, Meng Fang, Fanghua Ye, Ke Xu, Qin Zhang, Joey Tianyi Zhou, Mykola Pechenizkiy In this paper, we comprehensively investigate the vulnerabilities of contemporary LLMs to various social bias attacks, including prefix injection, refusal suppression, and learned attack prompts. Insights from the study contribute to the development of more inclusive and ethically responsible LLMs. |
| 3. | Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. We propose a novel approach, Diffusion-styled Preference Optimisation (DiffPO), providing an efficient and policy-agnostic solution for aligning LLMs with humans. |
| 4. | Aishik Nagar, Ishaan Singh Rawal, Mansi Dhanania*, Cheston Tan While transformer-based models have become the de facto standard for many language modelling tasks, little is known about how they represent compound words, and whether these representations are compositional. First, we evaluate compositionality in the representations by examining six diverse models of compositionality (addition, multiplication, dilation, regression, etc.). Surprisingly, we find that the classic vector addition model performs almost as well as any other model. |
| 5. | Ruirui Chen, Weifeng Jiang, Chengwei Qin, Cheston Tan As LLMs become increasingly integrated into our daily lives, it is crucial to assess and enhance their capacity to interpret and respond to human mental states. In this paper, we review LLM’s theory of mind (ToM) capabilities by examining both evaluation benchmarks and strategies designed to improve them. |
| 6. | Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu LLMs demonstrate remarkable capabilities across various domains, especially mathematics and logic reasoning. However, current evaluations overlook physics-based reasoning - a complex task requiring physics theorems and constraints. We present PhysReason, a novel and comprehensive benchmark for evaluating physics-based reasoning capabilities in LLMs. |
*denotes former CFAR student
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