Papers by Chao Feng
DisCo_Speech: Controllable Zero-Shot Speech Generation with A Disentangled Speech Codec (2026.acl-long)
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Tao Li, Wenshuo Ge, Zhichao Wang, Zihao Cui, Yong Ma, Yingying Gao, Chao Deng, Shilei Zhang, Junlan Feng
| Challenge: | DisCo-Speech is a zero-shot controllable text-to-speech framework . standard codecs entangle timbre and prosody, which hinders independent control in continuation-based LMs. |
| Approach: | They propose a disentangled speech codec and an LM-based generator to solve this problem . they propose fusion and reconstruction that merges content and prosody into unified tokens . |
| Outcome: | DisCo-Speech achieves competitive voice cloning and superior zero-shot prosody control. |
Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners (2024.emnlp-main)
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Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang
| Challenge: | Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages. |
| Approach: | They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods. |
| Outcome: | The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs. |
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024.lrec-main)
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| Challenge: | Existing work on document visual question answering fails to capture the differences and correlations between elements of a document and associated questions. |
| Approach: | They propose a document-visual question-answering challenge that exploits element-level semantics and employs hierarchical Graph structures to capture differences and correlations between elements. |
| Outcome: | The proposed model surpasses the state-of-the-art method and large language model in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness. |
Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models (2025.emnlp-main)
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Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, null Chenshaodong, Yingyi Zhang, Chao Feng, Ran Jiao
| Challenge: | Multi-modal keyphrase prediction (MMKP) aims to produce concise, informative phrases that capture the essence of cross-modal inputs. |
| Approach: | They propose to use vision-language models to generate conclusive phrases using multiple modalities of input information. |
| Outcome: | The proposed methods outperform existing methods on absence and unseen scenarios and overestimate model capability due to overlap in training tests. |
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance (2025.emnlp-main)
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| Challenge: | Visual Language Models (VLMs) have significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. |
| Approach: | They propose a framework to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model. |
| Outcome: | Empirical results show that the proposed framework improves the speed of the prediction task by 44%. |
Log-FGAER: Logic-Guided Fine-Grained Address Entity Recognition from Multi-Turn Spoken Dialogue (2023.emnlp-main)
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| Challenge: | Existing name entity recognition methods combine pre-trained language models with supervised models such as BiLSTM/LSTM-CRF to perform poorly in a spoken dialogue context. |
| Approach: | They propose a logic-guided fine-grained address recognition method that softly applies the logic rule to improve the accuracy of FGAER. |
| Outcome: | The proposed method improves fine-grained address entity recognition from multi-turn spoken dialogues. |
Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive Hardware-in-the-Loop Testing (2026.acl-industry)
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| Challenge: | Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. |
| Approach: | They propose to integrate semantic retrieval with domain-adapted large language models to support test engineers in real-world HIL workflows. |
| Outcome: | The proposed system improves perceived helpfulness, truthfulness, and satisfaction over general-purpose LLMs. |
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction (2025.findings-acl)
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| Challenge: | Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. |
| Approach: | They propose a method that rewrites the original instruction to achieve a jailbreak . they propose rewriting the original instructions to improve the attack strategy . |
| Outcome: | The proposed method is more efficient and easier to identify since no additional features are introduced. |
Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)
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| Challenge: | a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action . |
| Approach: | They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework . |
| Outcome: | The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com . |
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
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| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
| Approach: | They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors. |
| Outcome: | The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels. |
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance (2021.acl-long)
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
| Challenge: | Existing QA systems focus on unstructured text, structured knowledge base, or semi-structured tables. |
| Approach: | They propose a large-scale question answering model based on financial reports . numerical reasoning is usually required to infer the answer . |
| Outcome: | The proposed model achieves 58.0% inF1, an 11.1% increase over the baseline model, but still lags behind the best human model. |
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (2022.naacl-main)
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| Challenge: | Despite advances in self-supervised learning, there is a lack of models that can effectively capture both intra- and intra-item semantics for semi-structured session data. |
| Approach: | They propose a graph-based transformer model for semi-structured session data that captures both intra- and intra-item semantics. |
| Outcome: | The proposed model outperforms baselines in three session search and entity linking tasks by up to 9%. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training (2025.findings-acl)
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Zhijun Wang, Jiahuan Li, Hao Zhou, Rongxiang Weng, Jingang Wang, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang
| Challenge: | Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. |
| Approach: | They investigate the existence of code-switching in the pre-training corpus and categorize it into four types within two quadrants. |
| Outcome: | The proposed approach improves performance across benchmarks and representation space. |
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | MultiPL is a special case of multiple natural languages and requires limited computational resources to generate multilingual code. |
| Approach: | They propose to extend LLMs by combining two paired experts to optimize expert selection at token and segment levels. |
| Outcome: | The proposed extension improves the performance of the base LLMs while retaining the most popular ones using limited computational resources. |
Speech-based Slot Filling using Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have shown an unprecedented ability across various language tasks. |
| Approach: | They propose to use prompts and LoRA fine-tuning to improve slot filling robustness . they propose a linearised knowledge injection scheme to integrate dynamic external knowledge into LLMs. |
| Outcome: | The proposed model improves slot filling with noisy ASR transcriptions with 6.7% and 17.6% absolute SLU-F1 improvements compared to a fully fine-tuned Flan-T5-XL model. |
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners (2025.naacl-long)
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| Challenge: | Large language models have demonstrated impressive reasoning capabilities across multiple languages, but the relationship between capabilities in different languages is less explored. |
| Approach: | They decompose the process of reasoning tasks into two separate components: knowledge retrieval and knowledge-free reasoning. |
| Outcome: | The proposed model can be transferred across source-target languages despite secondary impact of resource in some specific target languages, while cross-lingual knowledge retrieval significantly hinders the transfer. |
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. |
Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding (2026.eacl-long)
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| Challenge: | Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts. |
| Approach: | They propose a lightweight auxiliary model trained with a GAE-inspired objective to predict final instruction-following quality from partial generations. |
| Outcome: | The proposed model achieves 10 points improvement in CLAP score over baseline AR models while maintaining computational parity with best-of-N decoding. |
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs (2024.findings-acl)
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Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Haorui Wang, Zhen Qin, Feng Han, Jialu Liu, Simon Baumgartner, Michael Bendersky, Chao Zhang
| Challenge: | Knowledge distillation (KD) is a technique for transferring expertise from large teacher models to compact student models with reduced memory footprints and inference costs. |
| Approach: | They propose to transfer knowledge from large teacher models to compact student models by exploiting teacher-student capacity discrepancies to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
| Outcome: | The proposed framework exploits teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. |
Masked Diffusion Captioning for Visual Feature Learning (2025.findings-emnlp)
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| Challenge: | Unlike autoregressive captioning, the strength of the visual learning signal in MDC does not depend on each token’s position in the sequence, reducing the need for auxiliary objectives. |
| Approach: | a decoder conditioned on visual features is trained to reconstruct the original text. |
| Outcome: | masked diffusion captioning (MDC) is a form of image-conditioned captioning that can be applied to visual tasks. |
Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA (2026.findings-acl)
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Guanhua Chen, Yutong Yao, Shenghe Sun, Ci-jun Gao, Shudong Liu, Lidia S. Chao, Feng Wan, Derek F. Wong
| Challenge: | Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their capability in visual procedure question answering (VP-QA) remains largely unexplored. |
| Approach: | They propose a multimodal benchmark specifically designed for visual procedural reasoning that synergizes cross-modal procedure retrieval, context-aware step decomposition, and the next step prediction. |
| Outcome: | The proposed framework significantly outperforms baselines on visual procedure question answering (VP-QA) Experiments on six VLMs show that it performs better than baselines. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
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 . |
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)
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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
| Challenge: | Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data. |
| Approach: | They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data. |
| Outcome: | The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. |
Understanding LLMs’ Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From (2025.emnlp-main)
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| Challenge: | Cross-lingual context retrieval is a fundamental aspect of cross-lingual alignment, but the performance and mechanism of it for large language models (LLMs) remains unclear. |
| Approach: | They evaluate cross-lingual context retrieval of over 40 large language models . they use cross-linguistic machine reading comprehension as a representative scenario . |
| Outcome: | The results show that open LLMs show strong cross-lingual context retrieval ability . the results also show that their oracle performances improve after training . |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)
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| Challenge: | Existing tasks to generate question-answer pairs from visual images are under-explored. |
| Approach: | They propose a task that targets question-answer pair generation from visual images. |
| Outcome: | The proposed model can generate diverse or consistent QAPs on two benchmarks. |
Self-attention-based Graph-of-Thought for Math Problem Solving (2025.findings-acl)
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| Challenge: | Existing methods for generating reasoning paths in a chain structure are inefficient and non-human-like. |
| Approach: | They propose a decoding method for a chain-based LLM that constructs a thought graph simultaneously as an LLM inference and generates reasoning steps with a graph-structured self-attention mechanism. |
| Outcome: | The proposed method improves reasoning accuracy without huge computational over-expensive LLMs and avoids performance degradation issues when the LLM is too small to comprehend complex prompts. |
Minimal, Local, and Robust: Embedding-Only Edits for Implicit Bias in T2I Models (2025.emnlp-main)
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| Challenge: | EmbEdit is a text-to-image editing method that only fine-tunes the word token embedding (WTE) of the target object. |
| Approach: | They propose a method to edit implicit assumptions and priors in text-to-image models without affecting unrelated objects or degrading overall performance. |
| Outcome: | The proposed method outperforms previous methods in various models, tasks, and editing scenarios. |
Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for encoding layout information rely on millions of learnable parameters . polar coordinates provide superior choice for layout modeling, study finds . |
| Approach: | They propose to model layout attention with Gaussian biases by feeding polar coordinates into 2-D Gausssian kernels. |
| Outcome: | The proposed model improves on three widely used benchmarks. |