Papers by Baizhou Huang
From TDMA to CDMA: A Multi-bit Watermark for Diffusion Language Models (2026.acl-long)
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| Challenge: | Existing multi-bit watermarking schemes cannot be directly applied to DLMs. |
| Approach: | They propose a multi-bit watermarking framework that encodes the entire watermark message holographically. |
| Outcome: | The proposed framework encodes the entire watermark message across all tokens holographically. |
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency (2025.findings-acl)
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| Challenge: | Existing benchmarks for knowledge editing in multimodal large language models focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge. |
| Approach: | They propose a decomposed definition of multimodal knowledge and a benchmark to evaluate it. |
| Outcome: | The proposed method reveals that it is difficult to define multimodal knowledge editing in LLMs. |
ALCUNA: Large Language Models Meet New Knowledge (2023.emnlp-main)
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| Challenge: | Existing benchmarks do not adequately measure large-scale language models’ capabilities when faced with new knowledge. |
| Approach: | They propose a benchmark called ALCUNA to evaluate LLMs' ability to handle new knowledge by altering existing entity attributes and relationships. |
| Outcome: | The proposed approach generates new knowledge by altering existing entity attributes and relationships, resulting in artificial entities distinct from real-world entities. |
B4: A Black-Box Scrubbing Attack on LLM Watermarks (2025.naacl-long)
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| Challenge: | Experimental results demonstrate superior performance of black-box scrubbing attack on watermarks compared with other baselines. |
| Approach: | They propose a black-box scrubbing attack on watermarks that embeds a hidden pattern invisible to human into generated content of a specific LLM. |
| Outcome: | The proposed method outperforms baselines in 12 different environments. |
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency (2024.acl-long)
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| Challenge: | Existing work utilizes verification properties to verify and re-rank solutions in a majority voting manner, but this assumption may not hold. |
| Approach: | They propose a multi-perspective self-consistency framework that incorporates both inter- and intra-consistency across outputs from multiple perspectives. |
| Outcome: | The proposed framework significantly boosts performance of foundation models on various benchmarks, including HumanEval (+15.91%), MBPP (+6.43%) and CodeContests (+9.37%). |
WaterPool: A Language Model Watermark Mitigating Trade-Offs among Imperceptibility, Efficacy and Robustness (2025.naacl-long)
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| Challenge: | Existing methods to trace the usage of large language models often face trade-offs between imperceptibility and robustness. |
| Approach: | They propose a key-centered scheme to unify existing methods by decomposing a watermark into two components: a 'key module' and a "mark module". |
| Outcome: | The proposed method can be integrated with existing methods and achieve near-optimal imperceptibility and detection efficacy. |
QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs (2026.acl-long)
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| Challenge: | a number of large language models (LLMs) require multi-bit watermarking to ensure provenance. |
| Approach: | They propose a multi-bit watermark that embeds messages within a continuous cumulative probability interval. |
| Outcome: | The proposed watermark breaks message symmetry in low-entropy decoding, showing it can be used for verification and quality verification. |
TriEmbed: Bridge the Gap between Text and Token Indices with Embedding Reparameterization (2025.findings-acl)
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| Challenge: | a current paradigm of language modeling discards linguistic relations between tokens during tokenization, creating a fundamental gap . empirical results show that TriEmbed provides more linguistically informative token embeddings . |
| Approach: | They propose a reparameterization method that incorporates morphological relationships . they propose to organize the vocabulary into a Trie structure to reparametrize embeddings . |
| Outcome: | Empirical results show that TriEmbed outperforms existing token embeddings while offering more linguistically informative token embeds. |