Papers by Chuan-Sheng Foo
SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks (2024.naacl-long)
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| Challenge: | Existing approaches to defend against word-level attacks have been limited. |
| Approach: | They propose a new approach called Semantic Robust Defence to enhance the robustness of language models by aligning the domains with a distance-based objective. |
| Outcome: | The proposed approach can be generalized across word embeddings, even when they share minimal overlap at both vocabulary and word-substitution levels. |
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data (2025.findings-acl)
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Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large language models (LLMs) have impressive performance but intellectual property concerns are looming . a framework that can be used to perform source attribution for LLMs can be developed. |
| Approach: | They propose a framework that enables an LLM to generate synthetic texts with embedded watermarks that contain information about their source. |
| Outcome: | The proposed framework achieves source attribution accuracy and robustness against adversaries. |
Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs (2024.emnlp-main)
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| Challenge: | Existing text watermarking methods are not robust enough against paraphrasing attacks . existing methods lack robustness to paraphrases and are not scalable to millions of users . |
| Approach: | They propose a training-free framework for robust and scalable text watermarking . they propose to use large language models as paraphrasers and a combination of techniques . |
| Outcome: | The proposed framework improves scalability, verifiability and computational efficiency compared to existing methods. |