Papers by Han Jiang

CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction (2021.acl-long)

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong

Teach Small Models to Reason by Curriculum Distillation (2025.emnlp-main)

Challenge:	Existing methods to integrate extracted knowledge from the Web to knowledge graphs (KGs) however, the predictions are made independently, which can be mutually inconsistent.
Approach:	They propose a relation integration model that aligns free-text relations to relations in a target KG . they propose combining two stages to make independent predictions and a collective model that accesses all candidate predictions.
Outcome:	The proposed model outperforms baseline models on two datasets and improves AUC from .677 to .748 and from 1.716 to 1.780.

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Wangyi Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

ProvBench: A Benchmark of Legal Provision Recommendation for Contract Auto-Reviewing (2025.acl-long)

Challenge:	Large Reasoning Models (LRMs) show strong System-2-style reasoning, but at the cost of significant computational overhead.
Approach:	They propose a two-stage curriculum distillation framework which builds a robust internal problem-solving student model and then teaches the student model to externalize this knowledge as explicit reasoning.
Outcome:	The proposed model outperforms single-stage baselines on mathematical benchmarks and significantly outperformed LRMs on complex tasks.

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Xiuxuan Shen, Zhongyuan Jiang, Junsan Zhang, Junxiao Han, Yao Wan, Chengjie Guo, Bingcheng Liu, Jie Wu, Renxiang Li, Philip S. Yu

Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)

Challenge:	Contract review is labor-intensive, time-consuming, and costly . a benchmark is proposed to detect potential legal conflicts .
Approach:	They propose a benchmark for legal provision recommendation and conflict detection for contract auto-reviewing which aims to recommend the legal provisions related to contract clauses and detect possible legal conflicts.
Outcome:	The proposed task recommends legal provisions related to contract clauses and detects legal conflicts.

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Songlin Yang, Yong Jiang, Wenjuan Han, Kewei Tu

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)

Challenge:	supervised dependency parsers can reach a very high accuracy, but they require treebanks for training.
Approach:	They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information.
Outcome:	The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset.

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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings .
Approach:	PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development .
Outcome:	PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines .

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)

Challenge:	Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging.
Approach:	They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model.
Outcome:	The proposed method is comparable to existing methods and comparable to those using historical data.

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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation"
Approach:	They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations.
Outcome:	The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC)

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)

Challenge:	Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs.
Approach:	They propose a system that extracts chemical reactions directly from raw scientific PDFs.
Outcome:	The proposed system can extract chemical reactions from raw scientific PDFs.

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Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Existing paper search systems lack detailed information to support finer-grained queries.
Approach:	They propose a paper-based index that transforms abstract-based corpus index into hierarchical index tree and offline can support paper search queries.
Outcome:	The proposed system achieves the SOTA performance and excels in fine-grained scenarios.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Huangyu Dai, Ben Chen, Kaidi Chen, Ying Han, Zihan Liang, Wen Jiang

Multilinguality

Challenge:	Neural machine translation (NMT) is pivotal for crosslingual conversation and trade . traditional solutions that penalize text redundancy or token reoccurrence have shown limited efficacy .
Approach:	They propose an algorithm that modulates suppression of tokens dynamically, informed by attention weights and inter-token distances.
Outcome:	The proposed algorithm outperforms existing methods in precision and generalizability.

CR-LLM: A Dataset and Optimization for Concept Reasoning of Large Language Models (2024.findings-acl)

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Nianqi Li, Jingping Liu, Sihang Jiang, Haiyun Jiang, Yanghua Xiao, Jiaqing Liang, Zujie Liang, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han

pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)

Challenge:	Existing concept reasoning related datasets suffer from modeledge leakage and context leakage.
Approach:	They propose a concept reasoning for large language models with modeledge leakage prevention and context leakage preventive methods to improve the models' conceptual reasoning abilities.
Outcome:	The proposed method significantly improves the existing models and reasoning methods, achieving a 7% increase in accuracy compared to CoT and showing better granularity.

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Han Zhang, Yunjiang Jiang, Mingming Li, Haowei Yuan, Yiming Qiu, Wen-Yun Yang

Stabilizing Efficient Reasoning with Step-Level Advantage Selection (2026.findings-acl)

Challenge:	Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval.
Approach:	They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items.
Outcome:	The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries.

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Han Wang, Xiaodong Yu, Jialian Wu, Jiang Liu, Ximeng Sun, Mohit Bansal, Zicheng Liu

s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)

Challenge:	Large language models generate long and verbose reasoning traces at inference time . short context post-training alone induces substantial reasoning compression .
Approach:	They propose a step-level advantage selection approach that reduces reasoning length by over 30% . they propose to use GRPO without any length-aware objective to train models in a shorter context window .
Outcome:	The proposed approach reduces average reasoning length by over 30% while improving Pass@1 accuracy by 3.79 points over the strongest length-aware baseline.

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Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

Challenge:	Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models.
Approach:	They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward.
Outcome:	The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples.

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang, Jimeng Sun, Jiawei Han

ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)

Challenge:	Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model.
Approach:	They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model.
Outcome:	The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets.

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Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong

Natural Language Interfaces

Challenge:	acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) .
Approach:	They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions.
Outcome:	The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets.

StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion (2025.naacl-long)

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Yinghao Aaron Li, Xilin Jiang, Cong Han, Nima Mesgarani

TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale (2024.naacl-long)

Challenge:	Recent advances in text-to-speech (TTS) models have led to improvements in speaker prosody and voices modeling.
Approach:	They propose an efficient zero-shot TTS model that leverages distilled time-varying style diffusion to capture diverse speaker identities and prosodies.
Outcome:	The proposed model surpasses state-of-the-art models in both naturalness and similarity while reducing inference speed by 90%.

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Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, Jiawei Han

Summarization

Challenge:	Large language models (LLMs) have advanced tasks like text summarization, but their size and computational demands limit their use in resource-constrained and privacy-centric settings.
Approach:	They propose a framework for distilling LLMs’ text summarization abilities into a compact, local model using a curriculum learning strategy that evolves from simple to complex tasks.
Outcome:	The proposed framework outperforms baseline models on CNN/DailyMail, XSum, and ClinicalTrial, and improves interpretability by providing insights into the summarization rationale.

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun

GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (2024.naacl-long)

Challenge:	Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem.
Approach:	They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules.
Outcome:	The results show that pre-trained language models are forgetful and pre-training leads to retentive models .

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Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han

CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions (2022.coling-1)

Challenge:	Existing relation extraction methods rely on exact matching with human-annotated reference relations, while GRE methods produce diverse and semantically accurate relations.
Approach:	They propose a multi-dimensional assessment of relation extraction methods using human-annotated reference relations.
Outcome:	The proposed method is consistent with human preferences for RE quality.

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Xinpeng Wang, Han Jiang, Zhihua Wei, Shanlin Zhou

Text Generation

Challenge:	Existing studies on story generation focus on coarse-grained control of the story, neglecting the details of the narrative.
Approach:	They propose a model for fine-grained control on the story that allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned.
Outcome:	The proposed method has strong controllability to generate customized stories according to the fine-grained personalized guidance.

Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)

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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

Challenge:	Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training.
Approach:	They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity.
Outcome:	The proposed framework outperforms baseline models while maintaining high Affinity.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong

Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)

Challenge:	Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages.
Approach:	They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English.
Outcome:	The proposed model outperforms open-source and Tibetan-focused models on diverse tasks.

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Hengtong Lu, Zhuoxin Han, Caixia Yuan, Xiaojie Wang, Shuyu Lei, Huixing Jiang, Wei Wu

Semantic Parsing

Challenge:	Existing work on slot filling uses labeled data from source domains to train a model for target domains.
Approach:	They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot.
Outcome:	The proposed method outperforms state-of-the-art models on multiple datasets and models.

Grounding Agent Memory in Contextual Intent (2026.findings-acl)

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Ruozhen Yang, Yucheng Jiang, Yueqi Jiang, Priyanka Kargupta, Yunyi Zhang, Jiawei Han

Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation (2024.findings-emnlp)

Challenge:	Large language models are deployed in long-horizon tasks that require agents to track interleaved goals, resolve references to prior information, and coordinate actions over extended trajectories.
Approach:	They propose an agentic memory system that indexes each trajectory step with a structured retrieval cue, contextual intent, and retrieves history by matching the current step’s intent.
Outcome:	The proposed system outperforms the strongest benchmark by 35.6%, with the largest gains as trajectory length increases.

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Siru Ouyang, Shuohang Wang, Minhao Jiang, Ming Zhong, Donghan Yu, Jiawei Han, Yelong Shen

QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)

Challenge:	Speculative decoding is a novel method to expedite inference in autoregressive (large) language models.
Approach:	They propose to use a smaller model as a draft model to speculate a block of tokens, which the target model then evaluates for acceptance.
Outcome:	The proposed method can be used to accelerate inference in autoregressive (large) language models by using smaller models as draft models to speculate tokens for multiple inference steps.

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Jingyao Li, Han Shi, Sitong Wu, Chuanyang Zheng, Zhenguo Li, Xin Jiang, Hong Xu, Jiaya Jia

ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision (2023.findings-acl)

Challenge:	Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics.
Approach:	They propose a system that captures relevant information within a fixed window size and provides precise answers to queries.
Outcome:	The proposed system can read Harry Potter within 30s and accurately answer the questions.

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Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, Jiawei Han

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Structured chemical reaction information is a vital tool for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
Approach:	They propose a method which utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions.
Outcome:	The proposed model outperforms baselines and outperformed existing models.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang

MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction (2024.emnlp-main)

Challenge:	Current outcome-centric verification paradigms neglect potential errors in the derivation process.
Approach:	They propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME.
Outcome:	The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models.

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Han Jiang, Junwen Duan, Zhe Qu, Jianxin Wang

ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)

Challenge:	Existing methods to extract text snippets from input text to support model predictions without explicit rationale annotation have limited their ability to capture meaningful internal correlations between aspects.
Approach:	They propose a multi-aspect rationale extractor that extracts text snippets to support model predictions without explicit rationale annotation.
Outcome:	The proposed method achieves state-of-the-art on two unsupervised rationale extraction benchmarks.

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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao

Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)

Challenge:	Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability.
Approach:	They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training.
Outcome:	The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks.

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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo

Question Answering

Challenge:	Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model.
Approach:	They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering.
Outcome:	The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies.

Taxonomy-guided Semantic Indexing for Academic Paper Search (2024.emnlp-main)

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SeongKu Kang, Yunyi Zhang, Pengcheng Jiang, Dongha Lee, Jiawei Han, Hwanjo Yu

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

Challenge:	Academic paper search often struggles to match underlying academic concepts between queries and documents.
Approach:	They propose a framework that extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy.
Outcome:	The proposed framework can be flexibly employed to enhance existing retrieval frameworks.

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

ToViLaG: Your Visual-Language Generative Model is Also An Evildoer (2023.emnlp-main)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Xinpeng Wang, Xiaoyuan Yi, Han Jiang, Shanlin Zhou, Zhihua Wei, Xing Xie

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

Challenge:	Recent large-scale Visual-Language Generative Models (VLGMs) generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks.
Approach:	They propose a bottleneck-based detoxification method to reduce toxicity while maintaining comparable generation quality.
Outcome:	The proposed method could reduce toxicity while maintaining comparable generation quality.

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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

DeTAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification (2025.findings-acl)

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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Yu Li, Han Jiang, Zhihua Wei

Robustness in NLP

Challenge:	Existing defense methods rely on fine-tuning or input modification, which suffer from limited generalization and reduced utility.
Approach:	They propose a finetuning-free approach that improves the defensive capabilities against jailbreak attacks of LLMs via targeted attention modification.
Outcome:	The proposed approach outperforms baselines in jailbreak defense and exhibits robust generalization across attacks and models, maintaining its effectiveness even on in-the-wild jailbreak data.

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Benchmarking Fine-Grained Error Detection in Multimodal Reasoning (2026.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Chi-Min Chan, Han Zhu, Chunyang Jiang, Jiaming Ji, Juntao Dai, Wei Xue, Sirui Han, Yike Guo

Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms (2023.emnlp-main)

Challenge:	Multimodal Process Reward Models (MPRMs) have emerged as a pivotal framework for enhancing the reasoning capabilities of Multimodal Large Language Models.
Approach:	They propose a benchmark specifically designed to evaluate MPRMs’ proficiency in detecting erroneous reasoning steps across diverse error categories.
Outcome:	The proposed model achieves up to 4.8% performance improvement through test-time scaling.

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Seungju Han, Junhyeok Kim, Jack Hessel, Liwei Jiang, Jiwan Chung, Yejin Son, Yejin Choi, Youngjae Yu

Multilingual Grammar Induction with Continuous Language Identification (D19-1)

Challenge:	NormLens is a visual-grounded framework for understanding commonsense norms . state-of-the-art models are not well-aligned with human annotation, we show .
Approach:	They propose a visual-grounded framework to study commonsense norms by NormLens . they find that models are not well-aligned with human annotation .
Outcome:	The proposed model judgments and explanations are not well-aligned with human annotations.

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Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu

A Regularization-based Framework for Bilingual Grammar Induction (D19-1)

Challenge:	Existing methods to induce grammars of multiple languages do not consider language similarity measures.
Approach:	They propose a universal grammar induction approach that captures similarity between languages . they use vector representations to capture similarity and softly tie grammar parameters .
Outcome:	The proposed approach performs well over monolingual and multilingual datasets.

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Yong Jiang, Wenjuan Han, Kewei Tu

Syntactic Text Processing

Challenge:	Existing multilingual grammar induction methods require external resources such as parallel corpora, word alignments or linguistic phylogenetic trees.
Approach:	They propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language.
Outcome:	The proposed method outperforms baselines on transfer grammar induction and bilingual grammar inducing on multiple languages.

EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)

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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han

100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability? (2025.findings-acl)

Challenge:	EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences.
Approach:	They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.
Outcome:	EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences.

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Van Yang, Hongye Jin, Shaochen Zhong, Song Jiang, Qifan Wang, Vipin Chaudhary, Xiaotian Han

Challenge:	Existing benchmarks for long-context capability are too synthetic and do not represent the real world usage of LLMs.
Approach:	They propose a length-controllable, real-life reflective benchmark that disentangles baseline knowledge from long-context capabilities.
Outcome:	Experiments show that the proposed benchmarks disentangle baseline knowledge from long-context capabilities.

Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)

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Kewei Tu, Yong Jiang, Wenjuan Han, Yanpeng Zhao

CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability (2025.emnlp-main)

Challenge:	Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations.
Approach:	This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse.
Outcome:	This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses.

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Han Peng, Jinhao Jiang, Zican Dong, Xin Zhao, Lei Fang

Question Answering

Challenge:	Existing methods to extend context length of Large Language Models (LLMs) still struggle with retrieval and reasoning in long context inputs.
Approach:	They propose a coarse-to-fine method to enhance multi-document question-answering capacities by removing background and distracting documents.
Outcome:	Experiments show that CAFE outperforms baseline methods on multiple documents.

Enhancing Unsupervised Generative Dependency Parser with Contextual Information (P19-1)

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Wenjuan Han, Yong Jiang, Kewei Tu

CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)

Challenge:	Existing approaches to unsupervised dependency parsing are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse.
Approach:	They propose a probabilistic model that generates a sentence and its parse from a latent representation, which encodes global contextual information of the generated sentence.
Outcome:	The proposed model achieves competitive accuracy compared with state-of-the-art models.

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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme

Topic Coverage-based Demonstration Retrieval for In-Context Learning (2025.emnlp-main)

Challenge:	CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview.
Approach:	They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses.
Outcome:	The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses.

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Wonbin Kweon, SeongKu Kang, Runchu Tian, Pengcheng Jiang, Jiawei Han, Hwanjo Yu

Low-Resource NLP

Challenge:	Prior methods to retrieve demonstrations based on embedding similarity or generation probability, resulting in irrelevant or redundant examples.
Approach:	They propose a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model.
Outcome:	The proposed framework covers all the necessary knowledge for the test input and the model.

Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)

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Han Jiang, Rui Wang, Zhihua Wei, Yu Li, Xinpeng Wang

Text Generation

Challenge:	Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles.
Approach:	They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets.
Outcome:	The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews.

A Survey of Unsupervised Dependency Parsing (2020.coling-main)

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Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu

SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)

Challenge:	Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees.
Approach:	They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends .
Outcome:	The proposed methods can be used in semantic parsing, machine translation, relation extraction, and many other tasks.

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Wenjia Jiang, Yiwei Wang, Boyan Han, Joey Tianyi Zhou, Chi Zhang

Programming Languages in NLP

Challenge:	Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning.
Approach:	They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation.
Outcome:	The proposed framework significantly improves accuracy over baselines on large-scale benchmarks.

Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)

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Wenjuan Han, Liwen Zhang, Yong Jiang, Kewei Tu

Robustness in NLP

Challenge:	Existing approaches to building effective adversarial attackers focus on classification problems.
Approach:	They propose a framework that learns to attack a structured prediction model with feedbacks from multiple reference models.
Outcome:	The proposed framework is able to attack state-of-the-art models and boost them with training . it is based on a sequence-to-sequence model with feedbacks from multiple reference models .

PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training (2023.emnlp-main)

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Yunyi Zhang, Minhao Jiang, Yu Meng, Yu Zhang, Jiawei Han

Zero-Shot Open-Schema Entity Structure Discovery (2026.eacl-long)

Challenge:	Existing methods for text classification use label names of target classes as the only supervision.
Approach:	They propose a method that uses keyword-based keyword matching to generate pseudo labels . they propose 'pieclass' module that iteratively trains classifiers and updates pseudo labels.
Outcome:	The proposed method achieves better performance than existing strong baselines on seven benchmark datasets and similar performance to fully-supervised classifiers on sentiment classification tasks.

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Xueqiang Xu, Jinfeng Xiao, James Barry, Mohab Elkaref, Jiaru Zou, Pengcheng Jiang, Yunyi Zhang, Maxwell J Giammona, Geeth De Mel, Jiawei Han

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)

Challenge:	Existing methods based on large language models (LLMs) rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results.
Approach:	They propose a novel approach to entity structure extraction that does not require any schema or annotated datasets.
Outcome:	Experiments show that ZOES improves LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method.

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Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang

Feasible is Not Enough: Cost-Aware Optimal Tool-Chain Planning on Multi-Solution Tool Graphs (2026.findings-acl)

Challenge:	Named entity recognition (NER) is a fundamental NLP task.
Approach:	They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers .
Outcome:	The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark.

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Liangliang Liu, Yanming Li, Yigang Liu, Jialong Han, Rujia Shen, Yi Guan, Yi Lin, Jingchi Jiang

ERNIE: Enhanced Language Representation with Informative Entities (P19-1)

Challenge:	Existing tools and benchmarks often form tool learning (TL) as a single-solution setting . exploring large-scale TG is computationally expensive, especially under constrained context budgets.
Approach:	They propose a framework for learning optimal TL policies over large tool graphs . they train a reinforcement learning agent to acquire transferable expansion skills .
Outcome:	The proposed framework improves task success and solution optimality by 46.21% and 66.34% on multiSoTLBench.

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Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, Qun Liu

Generative Personality Simulation via Theory-Informed Structured Interview (2026.eacl-long)

Challenge:	Existing pre-trained language models rarely consider incorporating knowledge graphs (KGs) Existing models capture rich semantic patterns from plain text and can be fine-tuned to improve performance of NLP tasks.
Approach:	They propose to incorporate knowledge graphs into pre-trained language models to enhance language representation with external knowledge.
Outcome:	The proposed model can take full advantage of lexical, syntactic, and knowledge information simultaneously.

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Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao, Frederick L. Oswald

Linguistics & Cognitive NLP

Challenge:	Personality structured interviews are often lacking in advancing social science research.
Approach:	They propose a method to incorporate psychological insights into LLM simulations . they use a measure theory grounded evaluation procedure to evaluate reliability and validity .
Outcome:	The proposed method improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes.

Improved Sparse Upcycling for Instruction Tuning (2025.coling-main)

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Wangyi Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

Challenge:	Existing methods for sparse upcycling lead to performance degradation in instruction tuning scenarios.
Approach:	They propose a representation-based approach to convert dense language models into sparsely activated ones by initializing router weights from language models.
Outcome:	The proposed architecture improves model capabilities and routing consistency across multiple benchmarks.

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

Forward-Backward Reasoning in Large Language Models for Mathematical Verification (2024.findings-acl)

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James Kwok

Structured Object Language Modeling (SO-LM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising (2024.emnlp-industry)

Challenge:	Extensive experiments on six standard mathematical data sets and three LLMs show that FOBAR achieves state-of-the-art performance.
Approach:	They propose to combine forward and backward reasoning to verify candidate answers . they propose to use a template to mask a number and ask the LLM to answer a backward question .
Outcome:	Experiments on mathematical data show that proposed backward reasoning outperforms Self-Consistency.

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Amir Tavanaei, Kee Kiat Koo, Hayreddin Ceker, Shaobai Jiang, Qi Li, Julien Han, Karim Bouyarmane