Papers by Han Jin

Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)

Challenge:	Existing models for text-rich networks do not take inter-document structure into account.
Approach:	They propose a pretraining framework for a text-rich network using a masked language model and a masking node prediction framework.
Outcome:	The proposed model outperforms baselines on four tasks in academic and e-commerce domains.

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Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han

Low-Resource NLP

Challenge:	Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect.
Approach:	They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances.
Outcome:	The proposed task significantly improves cost-effective zero-shot performance by large margins.

Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection (2024.findings-acl)

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Jaehoon Kim, Seungwan Jin, Sohyun Park, Someen Park, Kyungsik Han

Media Attitude Detection via Framing Analysis with Events and their Relations (2024.emnlp-main)

Challenge:	Existing models for implicit hate speech detection do not have significant advantage over cross-entropy loss-based learning.
Approach:	They propose a label-aware hard negative sampling strategy that encourages the model to learn detailed features from hard negative samples instead of random batch.
Outcome:	The proposed models outperform existing models for implicit hate speech detection both in- and cross-datasets.

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Jin Zhao, Jingxuan Tu, Han Du, Nianwen Xue

Challenge:	a recent study examined the effects of media framing on public perception and understanding of news articles.
Approach:	They propose to extract framing devices employed by media to assess their role in framating the narrative.
Outcome:	The proposed method surpasses baseline models and offers a more detailed and explainable analysis of media framing effects.

TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)

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Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks.
Approach:	They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements.
Outcome:	The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%.

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (2024.emnlp-main)

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Yu Zhang, Xiusi Chen, Bowen Jin, Sheng Wang, Shuiwang Ji, Wei Wang, Jiawei Han

A Regex Minimization Benchmark: A PSPACE-Complete Challenge for Language Models (2026.eacl-long)

Challenge:	Existing surveys on scientific LLMs focus on one or two fields or a single modality.
Approach:	They survey 260 scientific LLMs and examine their architectures and pre-training techniques . they also discuss commonalities and differences between LLM architectures .
Outcome:	The proposed model architectures and evaluation techniques are used to improve scientific discovery.

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Hyundong Jin, Joonghyuk Hahn, Yo-Sub Han

ERRV: Eliciting Efficient Reasoning through Reasoning Vectors for Policy Optimization in Large Language Models (2026.findings-acl)

Challenge:	Language models (LMs) have demonstrated impressive reasoning capabilities across domains . but their ability to handle PSPACE-complete problems remains underexplored . a new benchmark for regex minimization is proposed to evaluate LMs' reasoning capabilities .
Approach:	They propose a benchmark for regex minimization to evaluate LMs' reasoning power . they use a million regexes paired with their minimal equivalents to evaluate their performance .
Outcome:	The proposed model can solve NP-complete problems, but their ability to handle PSPACE-complete ones remains underexplored.

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Zhuowen Han, Lei Yang, Renren Jin, Dan Shi, Chenxi Sun, Deyi Xiong

Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)

Challenge:	Existing efforts to improve reasoning efficiency of large language models focus on modifying the reinforcement learning reward, such as adding length penalties.
Approach:	They propose a training framework that elicits efficient reasoning through reasoning vectors and a framework that allows the model to generate high-quality responses during reinforcement learning.
Outcome:	The proposed framework reduces reasoning length by 30% while maintaining stability, while retaining high accuracy.

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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen

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

Challenge:	Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness.
Approach:	They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio.
Outcome:	The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model .

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

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models (2026.acl-long)

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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Dan Shi, Zhuowen Han, Simon Ostermann, Renren Jin, Josef Van Genabith, Deyi Xiong

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

Challenge:	Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models beyond the training domain, while supervised fine-tuning (SFT) frequently leads to general capabilities forgetting.
Approach:	They propose a feature-level mechanistic analysis methodology to probe RL generalization using a controlled experimental setup.
Outcome:	The proposed method identifies a compact, task-agnostic set of features that directly mediate generalization across diverse tasks.

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Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety (2026.acl-long)

Challenge:	Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach:	They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome:	The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .

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Can Jin, Rui Wu, Tong Che, Qixin Zhang, Hongwu Peng, Jiahui Zhao, Zhenting Wang, Wenqi Wei, Ligong Han, Zhao Zhang, Yuan Cao, Ruixiang Tang, Dimitris N. Metaxas

Robustness in NLP

Challenge:	OpenAI introduces deliberative alignment (DA) to enhance safety of its o-series models, but effectiveness of this approach in open-source LLMs is understudied.
Approach:	They propose a case-augmented deliberative alignment method for large language models . they propose to use reinforcement learning on self-generated safety reasoning chains .
Outcome:	The proposed method avoids narrowly enumerated rules and allows broader adaptability.

LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)

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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

Challenge:	LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases .
Approach:	They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website .
Outcome:	The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks.

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

Neuronal Insights into LLM Attacks: Targeted Neuron Tuning for Precise and Robust Vulnerability Patching (2026.findings-acl)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Dan Shi, Renren Jin, Zhuowen Han, Yuqi Ren, Xinwei Wu, Zhigen Li, Deyi Xiong

Towards a Unified Paradigm of Concept Editing in Large Language Models (2025.emnlp-main)

Challenge:	Existing gradient-based attribution methods are inapplicable to adversarial attacks . et al.: Targeted neuron tuning improves model robustness against jailbreak attacks despite the model's vulnerability to jailbreak.
Approach:	They propose a gradient-based method to identify key neurons sensitive to adversarial behaviors in open-ended generation tasks.
Outcome:	The proposed method detects key neurons sensitive to adversarial behaviors in open-ended tasks.

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Zhuowen Han, Xinwei Wu, Dan Shi, Renren Jin, Deyi Xiong

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	Concept editing aims to control specific concepts in large language models (LLMs) however, there is a lack of rigorous theoretical analysis and a unified perspective to systematically understand and compare these methods.
Approach:	They propose a paradigm where conceptual injection is aligned at the neuron level.
Outcome:	The proposed paradigm offers a clear framework and valuable insights for advancing interpretability and controlled generation in large language models.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Information Extraction & Text Mining

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

Praetor: A Fine-Grained Generative LLM Evaluator with Instance-Level Customizable Evaluation Criteria (2025.acl-long)

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Yongqi Leng, Renren Jin, Yue Chen, Zhuowen Han, Ling Shi, Jianxiang Peng, Lei Yang, Juesi Xiao, Deyi Xiong

Challenge:	Existing evaluation methods are inadequate to evaluate large language models (LLMs).
Approach:	They propose a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria that can be used to evaluate large language models.
Outcome:	The proposed model outperforms existing LLM evaluators and instruction-tuned LLMs on multiple benchmarks and sets new SOTA results.

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

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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

PREDICT: Multi-Agent-based Debate Simulation for Generalized Hate Speech Detection (2024.emnlp-main)

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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Someen Park, Jaehoon Kim, Seungwan Jin, Sohyun Park, Kyungsik Han

Revisiting Entropy in Reinforcement Learning for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing methods to generalize hate speech detection models have been limited by the labeling criteria between datasets.
Approach:	They propose a framework that uses the concept of multi-agent for hate speech detection that uses a set of labeling criteria to create multiple agents based on the induced labeling of given datasets.
Outcome:	The proposed framework achieves superior cross-evaluation performance compared to methods that focus on specific labeling criteria or majority voting methods.

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Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong

Reasoning

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has emerged as a paradigm for enhancing the reasoning capabilities of large language models.
Approach:	They propose a positive-advantage reweighting approach that regulates model entropy by adjusting the loss weights assigned to tokens with positive advantages during RLVR training.
Outcome:	The proposed approach regulates model entropy by adjusting loss weights assigned to tokens with positive advantages during RLVR training while maintaining competitive performance.

TrapDoc: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents (2025.findings-emnlp)

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Hyundong Jin, Sicheol Sung, Shinwoo Park, SeungYeop Baik, Yo-Sub Han

Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)

Challenge:	a growing number of LLMs have been used to provide reasoning, writing, text-editing capabilities.
Approach:	They propose a method to inject imperceptible phantom tokens into LLMs to deceive users . the technique generates outputs that appear plausible to users but are in fact incorrect .
Outcome:	a new method injects imperceptible tokens into documents to deceive users . the proposed framework is compared to baselines to show its effectiveness .

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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang

Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)

Challenge:	Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models.
Approach:	They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data.
Outcome:	The proposed framework improves the factuality of LM generators by enhancing their training data.

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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, Shaoyi Chen

Information Extraction & Text Mining

Challenge:	Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks.
Approach:	They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm .
Outcome:	The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings.

Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Fan Li, Dong Yu, Yan Song, null Zhangfengzhen

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach:	They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome:	The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

Information Retrieval

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization (2026.acl-long)

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Yifeng Ding, Hung Le, Songyang Han, Kangrui Ruan, Zhenghui Jin, Varun Kumar, Zijian Wang, Anoop Deoras

Reasoning

Challenge:	Current reinforcement learning methods suffer from coarse-grained, trajectory-level rewards that provide insufficient learning signals for complex multi-turn interactions, leading to training stagnation.
Approach:	They propose a novel RL algorithm for training large language models for multi-turn tool-integrated reasoning (TIR) that incorporates three innovations: turn-level reward assignment that provides fine-grained feedback for individual turns, return-based advantage estimation where normalized discounted returns are calculated as advantages, and self-supervised reward shaping that exploits self-supervision signals from generated code to densify sparse binary outcome-based rewards.
Outcome:	The proposed algorithm outperforms GRPO by 3.0% across diverse math reasoning benchmarks and improves grepo by 3.9% on commonsense reasoning and program synthesis tasks.

Mondrian: A Framework for Logical Abstract (Re)Structuring (2025.emnlp-main)

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Elizabeth Grace Orwig, Shinwoo Park, Hyundong Jin, Yo-Sub Han

Challenge:	Using the ABT structure, academic abstracts are structured to provide clear and concise prose, but a lack of clarity and logical coherence is a challenge for authors struggling with English proficiency or academic writing conventions.
Approach:	They propose a framework that identifies the key components of an abstract and reorients itself to properly reflect the ABT logical progression.
Outcome:	The proposed framework improves comprehensibility of academic writing, particularly for non-native English speakers, and is based on a human evaluation and automated metrics.

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

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

Challenge:	Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty.
Approach:	They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline.
Outcome:	The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets.

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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.

Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)

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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han

Structured Data in NLP

Challenge:	Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems.
Approach:	They propose to augment large language models with text units retrieved from external knowledge corpora to alleviate the issue.
Outcome:	The proposed framework outperforms baselines on GRBench with three LLMs and shows that iterative reasoning outperformed the baselines.

SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Models (2026.eacl-industry)

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Jiaojiao Han, Wujiang Xu, Mingyu Jin, Mengnan Du

Explainability & Interpretability in NLP

Challenge:	Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque.
Approach:	They propose an agent-based framework that recasts feature interpretation from a passive, single-pass generation task into an explanation-driven process.
Outcome:	The proposed framework produces explanations with significantly higher generative and predictive accuracy compared to state-of-the-art baselines.

Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang

Rethinking Positional Encoding in Tree Transformer for Code Representation (2022.emnlp-main)

Challenge:	Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models.
Approach:	This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy .
Outcome:	The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications.

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Han Peng, Ge Li, Yunfei Zhao, Zhi Jin

Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)

Challenge:	Recent works have proposed novel tree Transformers to capture the syntactic structure in source code.
Approach:	They propose a novel tree Transformer encoding node positions based on a description method for tree structures to incorporate inductive bias into Transformer.
Outcome:	The proposed model outperforms baselines on code summarization and completion tasks across two languages, and it is able to perform better on both local and global paradigms.

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Zihao Li, Lecheng Zheng, Bowen Jin, Dongqi Fu, Baoyu Jing, Yikun Ban, Jingrui He, Jiawei Han

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)

Challenge:	Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains.
Approach:	They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision.
Outcome:	The proposed model can generalize graphs to unseen classes with weak text supervision.

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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun

Programming Languages in NLP

Challenge:	Current development practices face a dichotomy between automation and performance.
Approach:	They propose a framework to empower LLMs with the capability of automated explicit vectorization.
Outcome:	The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench.

A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows (2026.acl-industry)

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Han Yuxuan, Yuanxing Zhang, Yushuo Wang, Yichao Jin

Multilinguality

Challenge:	Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows.
Approach:	They propose a framework for structured information extraction from long, multilingual scanned financial documents . they combine image preprocessing, multilinguistic OCR, hybrid page-level retrieval and VLMs .
Outcome:	The proposed pipeline outperforms direct PDF-to-VLM baselines on 120 production KYC documents.

From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)

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Jialong Tang, Hongyu Lin, Meng Liao, Yaojie Lu, Xianpei Han, Le Sun, Weijian Xie, Jin Xu

Information Extraction & Text Mining

Challenge:	Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events.
Approach:	They propose a knowledge projection paradigm for event relation extraction using commonalities between events.
Outcome:	The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations.

Domain-Adaptive Pretraining Methods for Dialogue Understanding (2021.acl-short)

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Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song

Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation (2026.findings-acl)

Challenge:	Recent advances in pretraining methods have achieved promising results on NLP tasks . however, it is unclear which pretraining objective is the most effective for each downstream task .
Approach:	They evaluate the effectiveness of domain-adaptive pretraining objectives on downstream tasks . they use open-domain data to pretrain language models like BERT and SpanBERT .
Outcome:	The proposed model improves on two dialogue understanding tasks with domain-adaptive pretraining objectives.

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Yebo Wu, Han Jin, Zhijiang Guo, Li Li