Papers by Zhe Huang
A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)
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| Challenge: | a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework. |
| Approach: | They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. |
| Outcome: | The proposed framework surpasses conventional multi-task learning approaches in performance. |
TrInk: Ink Generation with Transformer Network (2025.emnlp-main)
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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu
| Challenge: | Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples. |
| Approach: | They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies. |
| Outcome: | The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models. |
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022.emnlp-main)
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| Challenge: | Recent pre-trained language models have produced impressive results, but there is still a gap between human written texts and machine-generated outputs. |
| Approach: | They propose a multi-task training strategy for long text generation grounded on the cognitive theory of writing. |
| Outcome: | The proposed model achieves better results on three open-ended generation tasks than baselines. |
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)
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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu
| Challenge: | Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. |
| Approach: | They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges. |
| Outcome: | The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods. |
Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization (2025.acl-long)
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| Challenge: | Video dubbing systems use neural machine translation and text-to-speech technologies to translate original speech into visual media programs. |
| Approach: | They propose a preference optimization method to optimize video dubbing duration alignment . they propose combining segment-wise sampling and fine-grained loss to mitigate duration mismatches . |
| Outcome: | The proposed method achieves superior performance in duration alignment tasks. |
Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective (2026.findings-acl)
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| Challenge: | Code Large Language Models (CLLMs) are reshaping how software is built, maintained, and evolved. |
| Approach: | They propose to use BPE tokenization to inadvertently leak code secrets . they propose to mitigate the gibberish bias by using a newer tokenizer . |
| Outcome: | The proposed model is based on a novel method that can be used to detect and mitigate gibberish bias in CLLMs. |
Are AI-Generated Text Detectors Robust to Adversarial Perturbations? (2024.acl-long)
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| Challenge: | Existing detectors for AI-generated text lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. |
| Approach: | They propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. |
| Outcome: | The proposed detector outperforms baseline methods on four datasets and is more generalizable in cross-domain, cross-genre, and mixed-source scenarios. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
Theory-optimal Quantization Based on Flatness (2026.acl-long)
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| Challenge: | Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision. |
| Approach: | They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations. |
| Outcome: | The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model. |
D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)
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| Challenge: | Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. |
| Approach: | They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing. |
| Outcome: | The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing. |
Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning (2026.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a low-cost alternative to full fine-timing due to the massive overhead. |
| Approach: | They propose a Mixture-of-Experts approach that enhances specialization while maintaining low resource overhead. |
| Outcome: | The proposed approach outperforms or matches state-of-the-art methods on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently . |
| Approach: | They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model. |
| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
Palette of Language Models: A Solver for Controlled Text Generation (2025.naacl-long)
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| Challenge: | Recent advances in large language models have revolutionized text generation with their remarkable capabilities. |
| Approach: | They propose to combine a single-attribute model with a discriminative model to achieve a combination strategy that incorporates positive correlation and attribute enhancement. |
| Outcome: | The proposed method is adapted for single-attribute control scenario and achieves surpassing results. |
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
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| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
| Approach: | They propose a novel generation framework that leverages autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. |
| Outcome: | The proposed framework outperforms baseline models and generates more coherent texts with richer contents. |
LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection (2026.acl-long)
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| Challenge: | Recent detection methods struggle to capture fine-grained semantic differences, especially for short texts. |
| Approach: | They propose a framework for machine-revised text detection that integrates two modules to enhance discriminative semantic features. |
| Outcome: | The proposed method outperforms existing detectors in identifying machine-revised text across diverse practical scenarios, tasks, and LLMs. |
LLM as a metric critic for low resource relation identification (2024.findings-emnlp)
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| Challenge: | Existing studies show that small language models (SLMs) overfit in low resource situations . however, the gap between pre-training and fine-tuning leads to performance decay . |
| Approach: | They propose to combine large language models and LLM for relation identification by co-evolution . they propose to use a masked language model prompt to generate a relation identification task . |
| Outcome: | The proposed model can handle low resource relation identification tasks with minimal overfitting . the proposed model provides essential background knowledge to assist training process . |
TIGEr: Text-to-Image Grounding for Image Caption Evaluation (D19-1)
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Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
An Entity-Driven Framework for Abstractive Summarization (D19-1)
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| Challenge: | Popular neural summarization models produce incoherent and unfaithful summaries . however, their outputs are often incohérent and incoerent . |
| Approach: | They propose a system for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on New York Times and CNN/Daily Mail datasets. |
Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction (2023.findings-acl)
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| Challenge: | Medical terms are difficult to understand and relations between medical entities become complicated. |
| Approach: | They propose to leverage medical domain knowledge for extracting entities and relations for Chinese medical texts by building a heterogeneous graph based on medical knowledge graph. |
| Outcome: | The proposed method is more effective than state-of-the-art methods on real Chinese medical texts. |