Papers by Wei Ji

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

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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

Unsupervised Morphological Tree Tokenizer (2025.findings-acl)

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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Qingyang Zhu, Xiang Hu, Pengyu Ji, Wei Wu, Kewei Tu

Syntactic Text Processing

Challenge:	Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information.
Approach:	They propose a method that uses morphological structure guidance to induce character-level structures of words by training a deep model.
Outcome:	Empirical results show that the proposed method retains complete morphemes and outperforms existing methods on morphological segmentation and language modeling tasks.

Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (2023.findings-acl)

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Peng Qi, Yuyang Zhao, Yufeng Shen, Wei Ji, Juan Cao, Tat-Seng Chua

Enhancing Large Language Models for Scientific Multimodal Summarization with Multimodal Output (2025.coling-industry)

Challenge:	Existing frameworks for detecting fake news videos are limited . a new approach is proposed to integrate neighborhood information of new videos .
Approach:	They propose a framework for automatically detecting fake news videos . it integrates neighborhood relationship of new videos belonging to same event .
Outcome:	The proposed framework improves performance of existing detectors and graph aggregation and debunking rectification modules.

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Zusheng Tan, Xinyi Zhong, Jing-Yu Ji, Wei Jiang, Billy Chiu

A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)

Challenge:	Scientific publications are becoming more multimedia, containing both text and visual content.
Approach:	They propose a framework for Scientific Multimodal Summarization with Multimodal Output . it leverages the power of large language models and extends its capability to cross-modal understanding .
Outcome:	The proposed framework outperforms uni- and multi-modality methods on two new datasets . it leverages the power of large language models and extends its capability to cross-modal understanding .

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Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen

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

Challenge:	Existing methods for document classification do not consider document filtering . existing methods do not include document filter.
Approach:	They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds .
Outcome:	The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words .

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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

PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)

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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Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)

Challenge:	Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks.
Approach:	They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework.
Outcome:	The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks.

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Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, Wei Lin

Natural Language Interfaces

Challenge:	Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems.
Approach:	They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Outcome:	The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist.

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models (2025.acl-long)

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Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Jianke Zhu, Wei Wang

LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)

Challenge:	Existing methods for offsite-tuning of large language models require high computational costs and lack theoretical analysis.
Approach:	They propose an offsite-tuning approach that selectively applies compression techniques such as rank compression and channel pruning to preserve the gradients of fine-tuned adapters while ensuring privacy.
Outcome:	The proposed method surpasses existing OT methods in privacy protection and model performance.

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Yi Lu, Xin Zhou, Wei He, Jun Zhao, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Green & Sustainable NLP

Challenge:	Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands.
Approach:	They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence .
Outcome:	The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence.

ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)

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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu

LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)

Challenge:	Existing studies focus on language-based premises and deduce valid conclusions from visual observations.
Approach:	They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning .
Outcome:	Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts .

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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding

Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration (2024.naacl-long)

Challenge:	Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges.
Approach:	They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs.
Outcome:	The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges .

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Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji

Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)

Challenge:	Existing work on LLMs that only enhance reasoning abilities, but which lack factual hallucination and slow-thinking capabilities, argues that SPP is a cognitive synergist.
Approach:	They propose a Solo Performance Prompting (SPP) that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
Outcome:	The proposed model reduces factual hallucination and maintains strong reasoning abilities on three challenging tasks .

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Mengying Yuan, WenHao Wang, Zixuan Wang, Yujie Huang, Kangli Wei, Fei Li, Chong Teng, Donghong Ji

Explainability & Interpretability in NLP

Challenge:	Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored.
Approach:	They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction.
Outcome:	The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios.

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

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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.

Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)

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Lu Ji, Zhongyu Wei, Jing Li, Qi Zhang, Xuanjing Huang

Representation Learning

Challenge:	Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring.
Approach:	They propose to identify argument pairs from two posts with opposite stances to a certain topic.
Outcome:	The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts .

You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations (2026.findings-acl)

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Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou, Yining Hua, Grace Ziqi Zhou, Haiyang Zhang, Tao Shen, Wei Wang, John Torous, Shaoxiong Ji, Ling Chen

Dialogue Systems & Conversational Agents

Challenge:	Psychological defenses are strategies people use to manage distress.
Approach:	They propose a dialogue corpus with help seeker utterances labeled for defense level and a DMRS Co-Pilot pipeline that provides evidence-based pre-annotations.
Outcome:	The proposed framework reduces annotation time by 24.0% in a counterbalanced study.

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, Tat-Seng Chua

Incorporating Causal Analysis into Diversified and Logical Response Generation (2022.coling-1)

Challenge:	Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges .
Approach:	They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs.
Outcome:	The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable.

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Jiayi Liu, Wei Wei, Zhixuan Chu, Xing Gao, Ji Zhang, Tan Yan, Yulin Kang

Text Generation

Challenge:	Existing generation-based models generate generic and safe responses such as "So am I" or "I don't know"
Approach:	They propose to predict the mediators to preserve relevant information and auto-regressively incorporate the mediator into generating process.
Outcome:	The proposed model generates relevant and informative responses and outperforms the state-of-the-art in terms of automatic metrics and human evaluations.

Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation (2023.findings-emnlp)

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Zixian Guo, Yuxiang Wei, Ming Liu, Zhilong Ji, Jinfeng Bai, Yiwen Guo, Wangmeng Zuo

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

Challenge:	Large vision-language models are often not open-source due to preventing abuse or commercial factors.
Approach:	They propose a method for parameter-efficient fine-tuning to improve model accessibility . large models are often not open-source due to preventing abuse or commercial factors . they propose implementing a lightweight adapter over the output feature of an inaccessible model .
Outcome:	The proposed methods improve on 11 benchmarks and are made publicly available.

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

GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation (2026.acl-long)

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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Jiarui Ji, Zehua Zhang, Zhewei Wei, Bin Tong, Guan Wang, Bo Zheng

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Social graphs provide high-quality supervision signals that encode local interactions and global network structure, yet they remain underutilized for LLM training.
Approach:	They propose a general LLM-based social graph simulation framework that leverages graph data as supervision for LLM training.
Outcome:	The proposed framework improves micro-level alignment by 6.1% on three real-world networks compared to the strongest baseline.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Low-Resource NLP

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)

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Yijiong Yu, Zhixiao Qi, Yongfeng Huang, Wei Wang, null Weifeng.liu, Ran Chen, Ji Pei

Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)

Challenge:	despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks .
Approach:	a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps .
Outcome:	The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts.

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Haozhe Ji, Pei Ke, Shaohan Huang, Furu Wei, Xiaoyan Zhu, Minlie Huang

Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths (2020.aacl-main)

Challenge:	Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph.
Approach:	They propose a method that leverages structural and semantic information of the knowledge graph to generate commonsense-aware text.
Outcome:	The proposed method outperforms baseline models on three text generation tasks that require reasoning over commonsense knowledge.

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Haozhe Ji, Pei Ke, Shaohan Huang, Furu Wei, Minlie Huang

Cross-modal Contrastive Attention Model for Medical Report Generation (2022.coling-1)

Challenge:	Existing tasks that use commonsense reasoning as multi-choice reading comprehension lack direct assessment to machine commonsence and impede its practicability to realistic scenarios.
Approach:	They propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation.
Outcome:	The proposed model outperforms the state-of-the-art models in automatic and human evaluation.

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Xiao Song, Xiaodan Zhang, Junzhong Ji, Ying Liu, Pengxu Wei

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)

Challenge:	Existing methods for medical report generation are unable to capture useful information from historical cases.
Approach:	They propose a model that captures both visual and semantic information from similar cases.
Outcome:	The proposed model outperforms the state-of-the-art models on almost all metrics on IU X-Ray and MIMIC-CXR benchmarks.

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Ruize Wang, Zhongyu Wei, Ying Cheng, Piji Li, Haijun Shan, Ji Zhang, Qi Zhang, Xuanjing Huang

DVAGen: Dynamic Vocabulary Augmented Generation (2025.emnlp-demos)

Challenge:	Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content.
Approach:	They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description.
Outcome:	The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset.

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Wei Du, Nuowei Liu, Jie Wang, Jiahao Kuang, Tao Ji, Xiaoling Wang, Yuanbin Wu

Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)

Challenge:	Existing dynamic vocabulary approaches struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations.
Approach:	They propose an open-source framework for training, evaluation, and visualization of dynamic vocabulary-augmented language models.
Outcome:	The proposed framework validates the effectiveness of dynamic vocabulary-augmented language models on modern LLMs and shows support for batch inference significantly improving inference throughput.

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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen

Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)

Challenge:	Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning.
Approach:	They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties.
Outcome:	The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.

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Lu Ji, Zhongyu Wei, Xiangkun Hu, Yang Liu, Qi Zhang, Xuanjing Huang

ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data (2025.findings-acl)

Challenge:	Existing research explores different text features of reply comments on word level and ignores interactions between participants.
Approach:	They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation.
Outcome:	The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply.

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Xinzhe Zheng, Sijie Ji, Jiawei Sun, Renqi Chen, Wei Gao, Mani Srivastava

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

Challenge:	Existing methods for mental health risk assessment rely on subjective textual records . however, these uncertainties can cause inconsistent and unreliable predictions .
Approach:	They propose a method that integrates objective behavior data alongside subjective mental records for robust mental health risk assessment.
Outcome:	The proposed approach achieves significant improvements over general LLMs.

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Visual Data in NLP

Challenge:	Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment.
Approach:	They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives.
Outcome:	The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.

Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition (D18-1)

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Tao Ge, Qing Dou, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Furu Wei, Ming Zhou

Multilinguality

Challenge:	a novel information network decipherment paradigm is proposed for fine-grained coordinated cross-lingual text stream alignment.
Approach:	They propose to use Burst Information Networks as media to represent text streams . they propose a simple yet effective information network decipherment algorithm with diverse clues .
Outcome:	The proposed approach outperforms existing approaches on bilingual lexicon extraction from coordinated text streams and can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining.

Enhancing Chinese Offensive Language Detection with Homophonic Perturbation (2025.emnlp-main)

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Junqi Wu, Shujie Ji, Kang Zhong, Huiling Peng, null Zhendongxiao, Xiongding Liu, Wu Wei

Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis (2026.acl-long)

Challenge:	Detecting offensive language in Chinese is challenging due to homophonic substitutions used to evade detection.
Approach:	They propose to use HED-COLD to build a large-scale homophonic dataset for Chinese offensive language detection and a homophone-aware pretraining strategy to learn phonetics and orthography.
Outcome:	The proposed framework achieves state-of-the-art performance on the COLD test set and the toxicity benchmark ToxiCloakCN.

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Huicong Li, Xiangbo Ji, Wei Wu

Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale (2024.acl-long)

Challenge:	Existing methods for semantic incongruence in sentiment analysis are limited by label-limited settings.
Approach:	They propose a framework for semi-supervised multimodal sentiment analysis that emphasizes stable cross-modal representations and reliable supervision.
Outcome:	The proposed framework outperforms state-of-the-art methods under label-limited settings.

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Xiang Hu, Pengyu Ji, Qingyang Zhu, Wei Wu, Kewei Tu

Building an Ellipsis-aware Chinese Dependency Treebank for Web Text (L18-1)

Challenge:	Existing syntactic language models require a gold tree and sequential training to generate sentences.
Approach:	They propose an unsupervised syntactic language model that incrementally generates a sentence with its syntaktic tree in a left-to-right manner.
Outcome:	The proposed model outperforms existing models on grammar induction and comprehension tasks while holding a substantial acceleration on training.

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Xuancheng Ren, Xu Sun, Ji Wen, Bingzhen Wei, Weidong Zhan, Zhiyuan Zhang

Syntactic Text Processing

Challenge:	ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance.
Approach:	They propose to use a Chinese dependency treebank to facilitate the parsing of web text . they propose to restore omissions and reserve contexts in the web text to improve dependency parsers .
Outcome:	The proposed framework enables the parsing of web text from online microblogs.

Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence (2025.emnlp-main)

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Yijiong Yu, Wei Wang, Ran Chen, Ji Pei

SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent (2024.findings-emnlp)

Challenge:	Existing methods for parallelizable reasoning tasks are inefficient, says a new study . generating lengthy reasoning sequences is computationally expensive and time-consuming, says the study authors .
Approach:	They propose a method that decodes multiple tokens per forward pass using a tree-like attention mask . their method achieves nearly 100% speedup in decoding while basically maintaining the answer quality .
Outcome:	Experimental results show that the method achieves nearly 100% speedup in decoding while maintaining the answer quality.

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Jiarui Ji, Yang Li, Hongtao Liu, Zhicheng Du, Zhewei Wei, Qi Qi, Weiran Shen, Yankai Lin

Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)

Challenge:	Existing research on the allocation of public scarce resources has limitations due to data scarcity and data scariness.
Approach:	They propose a framework that integrates Large Language Models into economic simulations . they conduct extensive policy simulation experiments to verify the framework's effectiveness .
Outcome:	The proposed framework bridges the gap between theoretical models and real-world dynamics by integrating large language models into economic simulations.

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Shengqiong Wu, Hao Fei, Wei Ji, Tat-Seng Chua

Multilinguality

Challenge:	Current captioning models are limited to the English language due to the largescale paired image-caption datasets.
Approach:	They propose to integrate the scene graph (SG) structures and the syntactic constituency trees into a captioner to improve captioning relevancy and fluency.
Outcome:	The proposed model improves captioning relevancy and fluency on English-Chinese transfers.

Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)

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Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua