Papers by Zhichao Wang
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. |
Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings (2025.acl-long)
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| Challenge: | Text embedding models are used for various natural language processing tasks such as sentiment analysis, text clustering, and content-based information retrieval. |
| Approach: | They propose a synthesis framework that leverages large language models to generate diverse negative samples with varying levels of similarity with the query. |
| Outcome: | The proposed framework achieves state-of-the-art performance surpassing existing synthesis strategies with synthetic data and when combined with public retrieval datasets. |
MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills (2026.eacl-long)
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Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu
| Challenge: | Current clinical LLM benchmarks fail to evaluate advanced clinical skills in AI and large language models (LLMs). |
| Approach: | They propose a framework to evaluate large language models (LLMs) using two instruction-following tasks designed to reflect real clinical scenarios. |
| Outcome: | The proposed framework evaluates LLMs through two instruction-following tasks designed to reflect real clinical scenarios. |
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)
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| Challenge: | Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. |
| Approach: | They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools. |
| Outcome: | The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. |
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events. |
| Approach: | They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context. |
| Outcome: | The proposed framework reduces decline rate while maintaining similar attack success rate. |
SLOT: Structuring the Output of Large Language Models (2025.emnlp-industry)
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| Challenge: | Structured outputs are essential for large language models (LLMs) but often deviate from predefined schemas hampering reliable application development. |
| Approach: | They propose a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. |
| Outcome: | The proposed model-agnostic approach transforms unstructured LLM outputs into precise structured formats. |
Enhancing Chain-of-Thought Reasoning via Neuron Activation Differential Analysis (2025.emnlp-main)
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| Challenge: | Existing studies focus on optimizing external components of CoT, but lack internal explanations for the quality of the model's outputs. |
| Approach: | They propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. |
| Outcome: | The proposed method shows that neurons in the feed-forward layers are critical in the generation of high-quality reasoning chains. |
Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)
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| Challenge: | Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models. |
| Approach: | They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding. |
| Outcome: | The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks. |
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025.acl-long)
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Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou
| Challenge: | Existing RAG systems struggle with the quality of retrieval documents, causing performance degradation and reducing performance. |
| Approach: | They propose a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents. |
| Outcome: | The proposed framework outperforms existing RAG frameworks in QA benchmarks and shows superior answer consistency and answer accuracy over baseline methods. |
A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)
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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
| Challenge: | Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices. |
| Approach: | They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features. |
| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
In-Context Example Ordering Guided by Label Distributions (2024.findings-naacl)
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| Challenge: | Prior work has shown that ICL is sensitive to different natural language instructions and different orderings of in-context examples. |
| Approach: | They propose two principles for in-context example ordering guided by model’s probability predictions. |
| Outcome: | The proposed model outperforms baseline models on 13 text classification datasets and nine autoregressive LLMs with 700M to 13B parameters. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion (2024.acl-long)
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| Challenge: | Existing LM-based VC models require offline conversion from source semantics to acoustic features, limiting their deployment to real-time applications. |
| Approach: | They propose a streaming LM-based model for zero-shot voice conversion that uses a fully causal context-aware LM with a temporal-independent acoustic predictor to facilitate real-time conversion given arbitrary speaker prompts and source speech. |
| Outcome: | The proposed model achieves comparable performance to non-streaming VC systems while maintaining a fully causal context-aware LM with a temporal-independent acoustic predictor. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)
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Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
| Challenge: | Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. |
| Approach: | They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content. |
| Outcome: | The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba). |
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, but their application to complex, multi-step, and long-horizon tasks remains challenging. |
| Approach: | They propose a framework that provides a finer-grained advantage assignment derived solely from outcome rewards. |
| Outcome: | The proposed framework provides a finer-grained advantage assignment, derived solely from outcome rewards. |
Diffusion Language Model Inference with Monte Carlo Tree Search (2026.findings-eacl)
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Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing methods for inference use heuristics to determine which positions to unmask and which tokens to commit . MEDAL is an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference. |
| Approach: | They propose a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference. |
| Outcome: | The proposed framework achieves 22.0% improvement over existing inference strategies across multiple benchmarks. |
Reinforcement Learning for Self-Improving Agent with Skill Library (2026.acl-long)
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Jiongxiao Wang, Qiaojing Yan, Yawei Wang, Yijun Tian, Soumya Smruti Mishra, Zhichao Xu, Megha Gandhi, Panpan Xu, Lin Lee Cheong
| Challenge: | Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. |
| Approach: | They propose a Reinforcement Learning-based approach to enhance agents’ self-improvement capabilities with a skill library. |
| Outcome: | The proposed framework achieves 8.9% higher Scenario Goal Completion when applied to supervised-finetuned model with expert experience while requiring 26% fewer interaction steps and generating 59% fewer tokens. |
FocusLLM: Precise Understanding of Long Context by Dynamic Condensing (2025.acl-long)
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Zhenyu Li, Yike Zhang, Tengyu Pan, Yutao Sun, Zhichao Duan, Junjie Fang, Rong Han, Zixuan Wang, Jianyong Wang
| Challenge: | Existing context condensing methods cannot accurately understand the full context, as there is a considerable amount of information loss in the condensed process. |
| Approach: | They propose a framework to extend the fixed context length of any decoder-only LLM by distilling crucial information from long sequences. |
| Outcome: | The proposed framework extends the fixed context length of any decoder-only LLM, allowing it to focus on relevant information from very long sequences. |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)
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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen
| Challenge: | Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody . |
| Approach: | They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis. |
| Outcome: | The proposed datasets provide richer contextual information, which is lacking in existing datasets. |
Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)
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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang
| Challenge: | Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications. |
| Approach: | They propose a framework that incorporates large language models to improve EA. |
| Outcome: | The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency. |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
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| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models (2025.acl-long)
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Hieu Tran, Zonghai Yao, Zhichao Yang, Junda Wang, Yifan Zhang, Shuo Han, null Feiyun Ouyang, Hong Yu
| Challenge: | Existing work aims to improve reasoning accuracy and factual integrity across large language models for knowledge-intensive tasks such as medical and commonsense reasoning. |
| Approach: | They propose a versatile extension to the mutual reasoning framework (rStar) that enhances reasoning accuracy and factual integrity across large language models. |
| Outcome: | The proposed extension to the mutual reasoning framework improves reasoning accuracy and factual integrity across large language models for complex, knowledge-intensive tasks. |
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs (2026.acl-long)
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| Challenge: | Large language models (LLMs) exhibit strong capabilities in reasoning, coding, and complex generation, yet their performance is highly sensitive to prompt design. |
| Approach: | They propose an API-only framework that decomposes a single prompt into semantic factors and updates selected factors while freezing the rest. |
| Outcome: | The proposed framework outperforms strong baselines, improves accuracy by up to +4.29 percentage points on average, and reduces optimization cost by 45–87% tokens on MultiArith while reaching peak validation in 1 step. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026.findings-acl)
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Zhichao Shi, Xuhui Jiang, Chengjin Xu, Cangli Yao, Shengjie Ma, Yinghan Shen, Zixuan Li, Jian Guo, Yuanzhuo Wang
| Challenge: | Current evaluation methods for large language models rely on static benchmarks . limited knowledge coverage and fixed difficulties hinder the targeted optimizations resulting in superficial evaluations of LLMs - a problem that has been addressed by JudgeAgent . |
| Approach: | They propose a knowledge-driven and dynamic evaluation framework for large language models . judgeAgent leverages LLM agents equipped with context graphs to traverse knowledge structures . |
| Outcome: | The proposed framework can achieve comprehensive evaluations and facilitate effective model iterations. |
MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)
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| Challenge: | Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings. |
| Approach: | They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment. |
| Outcome: | The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment. |
DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)
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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo
| Challenge: | Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities. |
| Approach: | They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training. |
| Outcome: | The proposed framework achieves an optimal balance between generation efficiency and data quality. |