Papers by Yiwen Zhang
TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding (2026.acl-long)
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| Challenge: | a critical ambiguity persists regarding what constitutes "joint ASR and diarization" a unified framework for multi-speaker ASR is proposed, but it is not yet clear what constitute "diarization." |
| Approach: | They propose a unified LLM-based framework that uses Temporal Anchor Grounding for joint multi-speaker ASR and diarization. |
| Outcome: | The proposed framework improves on AMI and AliMeeting benchmarks on speaker-content alignment . the proposed framework achieves consistent improvements in Diarization Error Rate over strong baselines . |
DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes. |
| Approach: | They propose a framework that uses a denoising diffusion process to complete reasoning tasks . they propose introducing a noise source and historical conditionguiding mechanism to improve interpretability . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)
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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
| Challenge: | Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions. |
| Approach: | They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification. |
| Outcome: | The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% . |
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)
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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored. |
| Approach: | They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations. |
| Outcome: | The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. |
Counteracting the Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing (2026.acl-long)
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Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai, Xiaowei Shi, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large vision language models have impressive reasoning capabilities across complex multimodal tasks. |
| Approach: | They propose to use distribution-reshaping and trajectory-rebalancing to improve visual reasoning capabilities. |
| Outcome: | Experiments on Qwen2-VL-7B-Instruct and InternVL2.5-4B models show that their methods outperform baselines by 3.86 points. |
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)
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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations. |
| Approach: | They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries. |
| Outcome: | The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
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| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)
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| Challenge: | Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity. |
| Approach: | They propose a method that introduces structured polynomial expansion directly into the low-rank factor space. |
| Outcome: | The proposed method outperforms state-of-the-art methods across diverse benchmarks. |
DDPrompt: Differential Diversity Prompting in Large Language Models (2024.acl-short)
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| Challenge: | Large Language Models (LLMs) have shown that their reasoning ability can be enhanced through approaches like Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a method that generates differentially diverse reasoning paths for different types of questions by voting on the optimal prompts. |
| Outcome: | The proposed method improves LLMs' reasoning ability on complex reasoning tasks by learning from demonstrations while keeping their parameters frozen. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications. |
| Approach: | They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks. |
| Outcome: | The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)
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| Challenge: | balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications. |
| Approach: | They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers . |
| Outcome: | The proposed framework reduces training cost while maintaining general capabilities . it can be open-sourced upon publication. |
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)
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| Challenge: | Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance. |
| Approach: | They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing. |
| Outcome: | The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance. |
DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)
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| Challenge: | Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships. |
| Approach: | They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. |
| Outcome: | The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions. |
Learn to Adapt for Generalized Zero-Shot Text Classification (2022.acl-long)
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| Challenge: | Existing methods for generalized zero-shot text classification generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes. |
| Approach: | They propose a network that trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning scenario. |
| Outcome: | The proposed model outperforms several previous approaches on five text classification datasets. |
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries. |
| Approach: | They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks. |
DenseLoRA: Dense Low-Rank Adaptation of Large Language Models (2025.acl-long)
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| Challenge: | Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization. |
| Approach: | They propose a low-rank adaptation approach that fine-tunes two low-ranked matrices and adapts them through a dense low-Rank matrix, improving parameter utilization and adaptation efficiency. |
| Outcome: | The proposed approach achieves 83.8% accuracy with only 0.01% of trainable parameters compared to LoRA's 80.8% with 0.70% of trainability parameters on LLaMA3-8B. |
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)
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| Challenge: | Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies. |
| Approach: | They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway. |
| Outcome: | Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization. |
Distill Visual Chart Reasoning Ability from LLMs to MLLMs (2025.findings-emnlp)
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Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | a new method for generating chart annotations is proposed to improve visual reasoning in multimodal large language models. |
| Approach: | They propose a code-as-intermediary translation method for distilling visual reasoning abilities from LLMs to MLLMs. |
| Outcome: | The proposed method is cost-effective, efficient and scalable. |
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)
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| Challenge: | Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes. |
| Approach: | They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations . |
| Outcome: | The proposed model yields competitive, transferable representations, while captioning exhibits better scalability. |
Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval (2023.findings-emnlp)
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| Challenge: | Prior work typically encodes all tokens in articles using pre-trained language models, however, many named entities are difficult to accurately recognize and predict by language models. |
| Approach: | They propose an ENtity-aware article GeneratIoN and rEtrieval framework to explicitly incorporate named entities into language models. |
| Outcome: | The proposed framework can boost article generation and retrieval performance, with a 4-5 perplexity improvement in article generation, and a 3-4% boost in recall@1 in article retrieval. |
DR-Arena: an Automated Evaluation Framework for Deep Research Agents (2026.acl-long)
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| Challenge: | Existing benchmarks for evaluating deep research capabilities rely on static datasets. |
| Approach: | They propose a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation. |
| Outcome: | DR-Arena achieves a Spearman correlation of 0.94 with the LMSYS Search Arena leaderboard. |