Papers by Wei Ding

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models (2026.acl-long)

Challenge:	Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say .
Approach:	They propose to use syntactically simple sentences to test the inference ability of NLI models.
Outcome:	The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair.

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Peihua Mai, Xuanrong Gao, Youlong Ding, Xianglong Du, Wei Liu, Yan Pang

Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)

Challenge:	Existing privacy-preserving inference methods sacrifice utility or efficiency, authors say . current approaches suffer a trilemma between privacy, utility, and efficiency, they say .
Approach:	They propose a model-agnostic framework for privacy-preserving LLM inference that reformulates privacy protection at the batch level rather than the individual-prompt level.
Outcome:	The proposed model-agnostic framework achieves 20% higher utility than previous models . it reduces query cost by up to 5 compared to non-batched inference .

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Jianhao Chen, Haoyuan Ouyang, Junyang Ren, Wentao Ding, Wei Hu, Yuzhong Qu

Low-Resource NLP

Challenge:	Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences.
Approach:	They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text.
Outcome:	The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset.

Temporal Scaling Law for Large Language Models (2025.emnlp-main)

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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding

EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models (2025.coling-main)

Challenge:	Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size.
Approach:	They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law .
Outcome:	The proposed model predicts the test loss of LLMs as the training steps scale up.

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Zeliang Tong, Zhuojun Ding, Wei Wei

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

Challenge:	Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data.
Approach:	They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement.
Outcome:	The proposed framework shows consistent performance improvements on four benchmarks.

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

Reasoning

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)

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Haochen Tan, Han Wu, Wei Shao, Xinyun Zhang, Mingjie Zhan, Zhaohui Hou, Ding Liang, Linqi Song

MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)

Challenge:	Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information.
Approach:	They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary.
Outcome:	The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets.

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Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng

SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning (2026.findings-acl)

Challenge:	Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning.
Approach:	They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods.
Outcome:	The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages.

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Kehua Feng, Keyan Ding, Yuhao Wang, Menghan Li, Fanjunduo Wei, Xinda Wang, Huajun Chen

Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)

Challenge:	Existing alignment methods struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks.
Approach:	They propose a framework for 'Safety' alignment via eFficient' Ex-Ante-R**easoning that instantiates structured Ex-Ance reasoning and embeds predefined safety rules.
Outcome:	The proposed framework enhances safety performance while maintaining usefulness and efficiency.

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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)

Challenge:	Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations.
Approach:	They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries.
Outcome:	The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision.

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Zhuojun Ding, Wei Wei, Chenghao Fan

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Challenge:	Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains.
Approach:	They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains.
Outcome:	The proposed framework outperforms the unified model by 10% on multiple benchmarks.

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization (2025.emnlp-demos)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Dawei Xiang, Wenyan Xu, Kexin Chu, Tianqi Ding, Zixu Shen, Yiming Zeng, Jianchang Su, Wei Zhang

Low-Resource NLP

Challenge:	PromptSculptor automates the iterative prompt optimization process for Text-to-Image models . previous work focused on generating detailed, high-quality prompts based on user feedback .
Approach:	They propose a framework that decomposes a task into four specialized agents . they use Chain-of-Thought reasoning to transform a short, vague user prompt into a comprehensive, refined prompt.
Outcome:	The proposed framework significantly improves output quality and reduces iterations needed for user satisfaction.

\mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts (2024.acl-long)

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Yifeng Ding, Jiawei Liu, Yuxiang Wei, Lingming Zhang

Reasoning

Challenge:	Existing studies focus on the data perspectives of instruction tuning, leaving room for exploring advanced training schemes.
Approach:	They argue that prior works overlook the possibility of improving code instruction tuning by advancing existing training schemes.
Outcome:	The proposed model is dense because all parameters are activated to predict the next token (assuming it is a decoder-only LLM).

LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)

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Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Question Answering

Challenge:	Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications.
Approach:	They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks.
Outcome:	The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings.

ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models (2025.acl-long)

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Hanxing Ding, Shuchang Tao, Liang Pang, Zihao Wei, Jinyang Gao, Bolin Ding, Huawei Shen, Xueqi Cheng

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

Challenge:	Existing approaches to tool learning rely on hand-crafted prompts and natural language reasoning, making multi-step planning difficult and lacking precise error diagnosis and reflection mechanisms.
Approach:	They propose a framework that reformulates tool learning as a code generation task.
Outcome:	The proposed framework achieves superior performance in task completion accuracy and execution reliability compared to existing approaches.

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

CAST: Achieving Stable LLM-based Text Analysis for Data Analytics (2026.findings-acl)

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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Jinxiang Xie, Zihao Li, Wei He, Rui Ding, Shi Han, Dongmei Zhang

Low-Resource NLP

Challenge:	Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labeling.
Approach:	They propose a framework that enhances output stability by constraining the model’s latent reasoning trajectory.
Outcome:	The proposed framework improves stability by constraining the model's latent reasoning trajectory.

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)

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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin

Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation (2026.findings-acl)

Challenge:	Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge .
Approach:	They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups.
Outcome:	The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance .

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Chen Lifan, Wei Ding, Jingwen Yang, Xiuze Zhou, Jingan Chen, Fan Lin

AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs (2026.acl-long)

Challenge:	Existing approaches to financial report generation are insufficient to handle dynamic uncertainties of real-world financial environments.
Approach:	They propose a cognitively grounded agentic framework for professional financial report generation that is driven by Dynamic Graph of Thoughts and a social collaboration mechanism to facilitate coordinated agent interaction.
Outcome:	The proposed framework is based on a dynamic reasoning model and social collaboration mechanism.

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Xuanwen Ding, Chengjun Pan, Zejun Li, Jiwen Zhang, Siyuan Wang, Zhongyu Wei

Green & Sustainable NLP

Challenge:	Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and cross-modality complexity.
Approach:	They propose an adaptive evaluation framework for efficient benchmarking that treats evaluation as an interview-like process by keeping a hypothesized ability structure of the evaluated model and actively selecting the informative questions.
Outcome:	Experiments on four representative multimodal benchmarks show that **A2-Judger significantly improves sample efficiency while maintaining reliable evaluation results.

A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends (2026.findings-acl)

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Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, Yifan Peng

Information Integration in Large Language Models is Gated by Linguistic Structural Markers (2025.emnlp-main)

Challenge:	Visually Rich Document Understanding (VRDU) frameworks are a key area of research . early approaches to VRDU relied on manually crafted rules and domain-specific heuristics . conventional deep learning approaches do not integrate the diverse modalities in documents .
Approach:	They review recent advances in MLLM-based Visually Rich Document Understanding (VRDU) their findings highlight emerging trends and promising research directions .
Outcome:	The proposed frameworks are scalable, reliable, and adaptable, the authors argue . their findings highlight emerging trends and promising research directions .

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Wei Liu, Nai Ding

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

Challenge:	Existing studies show that LLMs can integrate information across multiple scales of language .
Approach:	They propose to quantify the information integration window of large language models and measure the information loss from relying solely on the local window.
Outcome:	The proposed method shows that the integration window of large language models is strongly modulated by sentence boundaries.

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang

Challenge:	Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning.
Approach:	They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts.
Outcome:	The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube.

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (D19-1)

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Jiwei Ding, Wei Hu, Qixin Xu, Yuzhong Qu

Natural Language Interfaces

Challenge:	Existing approaches build universal paraphrasing or ranking models for whole questions . current approaches build a universal ranking model for the whole questions, which fails for complex, long-tail questions.
Approach:	They propose a new query generation approach based on frequent query substructures which helps rank existing query structures or build new query structures.
Outcome:	The proposed approach significantly outperforms existing models on two benchmark datasets.

Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)

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Wei Huang, Yizhe Xiong, Xin Ye, Zhijie Deng, Hui Chen, Zijia Lin, Guiguang Ding

Reasoning

Challenge:	Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data.
Approach:	They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes .
Outcome:	The proposed framework preserves the benefits of token-level reasoning while reducing computational cost.

Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)

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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)

Challenge:	Currently, the Transformer is the de facto architecture of choice for processing sequential data.
Approach:	They evaluate the Transformer architecture and its modifications in a shared experimental setting . they conjecture that performance improvements may strongly depend on implementation details .
Outcome:	The proposed improvements do not significantly improve performance, the authors find . the proposed improvements are either developed in the same codebase or are minor changes .

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Peng Ding, Wen Sun, Dailin Li, Wei Zou, Jiaming Wang, Jiajun Chen, Shujian Huang

MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation.
Approach:	They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement.
Outcome:	The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase.

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Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, Xueqi Cheng, Tat-Seng Chua

Green & Sustainable NLP

Challenge:	Existing approaches to multi-aspect controllable text generation require expensive iteration / searching within the discrete text space during the decoding stage, resulting in a degradation of text quality due to the domain discrepancies between different aspects.
Approach:	They propose a framework that estimates compact latent space for multiple aspects and performs efficient Sampling with a fast sampler to eliminate domain discrepancies.
Outcome:	The proposed framework outperforms baselines on attribute relevance and textual quality while maintaining a high inference speed.

AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)

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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo

Challenge:	Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking.
Approach:	They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation.
Outcome:	The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts.

Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)

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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)

Challenge:	Recent studies show that Large language models struggle with handling long token sequences due to limited training context size.
Approach:	They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities.
Outcome:	The proposed method outperforms existing methods on 4 language modeling benchmarks.

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Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, Yang Wei

DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs (2025.emnlp-main)

Challenge:	Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication.
Approach:	They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness.
Outcome:	The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%.

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Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu

Charge-Based Prison Term Prediction with Deep Gating Network (D19-1)

Challenge:	Existing sparsification methods like pruning can lose model knowledge through parameter removal.
Approach:	They propose a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.
Outcome:	The proposed approach achieves superior performance across language modeling and downstream tasks under equivalent computational constraints.

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Huajie Chen, Deng Cai, Wei Dai, Zehui Dai, Yadong Ding

Challenge:	Existing work merely predicts the total prison term, but in reality a defendant is often charged with multiple crimes.
Approach:	They propose a charge-based prison term prediction task that better fits real needs and makes it more accurate and interpretable.
Outcome:	The proposed method achieves state-of-the-art performance for charge-specific feature selection and aggregation.

Better Modeling of Incomplete Annotations for Named Entity Recognition (N19-1)

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Zhanming Jie, Pengjun Xie, Wei Lu, Ruixue Ding, Linlin Li

Information Extraction & Text Mining

Challenge:	Existing approaches to named entity recognition (NER) assume that the training data is fully annotated with named entity information.
Approach:	They propose a supervised setup for named entity recognition where annotated data is assumed to be available during training.
Outcome:	The proposed approach is able to recognize named entities with incomplete annotations.

Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection (2025.acl-long)

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Mingyu Derek Ma, Yanna Ding, Zijie Huang, Jianxi Gao, Yizhou Sun, Wei Wang

Challenge:	Existing work has been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary.
Approach:	They propose to evaluate a set of tasks using decoding-free candidate selection methods on a comprehensive set of questions.
Outcome:	The proposed methods are evaluated on a set of tasks including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with 10k+ options.

From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)

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Feng Zhang, Wei Chen, Fei Ding, Meng Gao, Tengjiao Wang, Jiahui Yao, Jiabin Zheng

A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)

Challenge:	Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains.
Approach:	They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates.
Outcome:	The proposed model outperforms baselines that need further fine-tuning or domain-specific samples.

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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu

Challenge:	Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP.
Approach:	They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation .
Outcome:	The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave .

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)

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Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models (2024.findings-naacl)

Challenge:	Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries.
Approach:	They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation.
Outcome:	The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks.

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Zhenpeng Su, Zijia Lin, Baixue Baixue, Hui Chen, Songlin Hu, Wei Zhou, Guiguang Ding, Xing W

Debiasing LLMs by Masking Unfairness-Driving Attention Heads (2026.findings-acl)

Challenge:	Existing generative language models neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones.
Approach:	They propose a function to mitigate the imbalance between frequent and infrequent tokens . authors propose 'MiLe Loss' function to assess learning difficulty of tokens during training .
Outcome:	Experiments show that models with proposed model can improve on downstream benchmarks.

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Tingxu Han, Wei Song, Ziqi Ding, Ziming Li, Chunrong Fang, Yuekang Li, Dongfang Liu, Zhenyu Chen, Zhenting Wang

A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)

Challenge:	Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile.
Approach:	They propose a lightweight debiasing framework that detects bias heads and selectively masks only those heads that activate under DA and CoT.
Outcome:	The proposed framework reduces unfairness by 391.9%- 534.5% in both one- and two-turn dialogues.

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Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, Luo Si

Information Extraction & Text Mining

Challenge:	Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness.
Approach:	They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers.
Outcome:	The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities.

Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback (2025.coling-main)

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Feng Zhang, Wei Chen, Meng Gao, Fei Ding, Tengjiao Wang, Jiahui Yao, Jiabin Zheng

Sentiment Analysis

Challenge:	Recent studies on fine-grained intent detection have focused on collecting large-scale and high-quality samples via crowdsourcing resulting in data scarcity.
Approach:	They propose an iterative differential generation framework with contrastive feedback to generate high-quality pseudo samples and accurately capture the crucial nuances in target class distribution.
Outcome:	The proposed framework generates high-quality pseudo samples and captures crucial nuances in target class distribution.

Distill Visual Chart Reasoning Ability from LLMs to MLLMs (2025.findings-emnlp)

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Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang

Fusion or Defusion? Flexible Vision-and-Language Pre-Training (2023.findings-acl)

Challenge:	a new method for generating chart annotations is proposed to improve visual reasoning in multimodal large language models.
Approach:	They propose a code-as-intermediary translation method for distilling visual reasoning abilities from LLMs to MLLMs.
Outcome:	The proposed method is cost-effective, efficient and scalable.

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Rongyi Sun, Ziran Li, Yifeng Ding, Qifan Wang, Jingang Wang, Haitao Zheng, Wei Wu, Yunsen Xian