Papers by Yik-Cheung Tam

5 papers
VLA-Mark: A cross modal watermark for large vision-language alignment models (2025.emnlp-main)

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Challenge: Existing text watermarking methods disrupt visual-textual alignment, leaving semantic-critical concepts vulnerable.
Approach: They propose a vision-aligned framework that embeds detectable watermarks into outputs . they combine localized patch affinity, global semantic coherence, contextual attention patterns .
Outcome: The proposed framework shows lower PPL and higher BLEU than conventional methods with near-perfect detection (98.8% AUC).
Arithmetic Reasoning with LLM: Prolog Generation & Permutation (2024.naacl-short)

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Challenge: Existing work has shown that large language models can generate arithmetic and commonsense reasoning, but they are not native to mathematical operations and symbolic manipulations.
Approach: They propose to use large language models to generate Prolog programs to solve math problems using a code interpreter to generate arithmetic and symbolic formulas.
Outcome: The proposed model outperforms CoT generation in the GSM8K benchmark across three LLMs.
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation (P19-1)

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Challenge: Rhetoric is a vital element in modern Chinese poetry, and plays an essential role in improving its aesthetics. however, to date, it has not been considered in research on automatic poetry generation.
Approach: They propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation . their model captures various rhetorical patterns in an encoder and incorporates mixtures .
Outcome: The proposed model outperforms state-of-the-art methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.
Read and Comprehend by Gated-Attention Reader with More Belief (N18-4)

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Challenge: Existing approaches to read comprehension using gated-attention have been effective . collaborative gating and self-belief aggregation are proposed to address these assumptions .
Approach: They propose to use a document-to-query attention system to gate token encodings of a query . they conjecture that query tokens other than the cloze token may be informative .
Outcome: The proposed approaches advance the state-of-the-art results in CNN, Daily Mail, and Who Did What public test sets.
Predicate-Guided Generation for Mathematical Reasoning (2025.emnlp-main)

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Challenge: Experimental results show that Prolog-MATH generates 81.3% solution coverage on Deepseek-V3 .
Approach: They propose a curated corpus to support mathematical reasoning in large language models . they propose supervised fine-tuning followed by GRPO training to address problems that Deepseek-V3 fails to solve.
Outcome: The proposed pipeline achieves 81.3% solution coverage on the Deepseek-V3 training set.

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