Papers by Lan Li

Depression Detection on Social Media with Large Language Models (2025.emnlp-industry)

Challenge:	Existing knowledge graph embedding techniques suffer from high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations.
Approach:	They propose a knowledge graph embedding framework that leverages mutual information maximization to improve the semantic representation of entities and relations.
Outcome:	Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with consistent performance improvements across various baseline models.

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Xiaochong Lan, Zhiguang Han, Yiming Cheng, Li Sheng, Jie Feng, Chen Gao, Yong Li

VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)

Challenge:	Existing methods for analyzing social media data lack a systematic integration of medical knowledge, causing a critical treatment gap.
Approach:	They propose a framework that leverages Large Language Models to integrate medical knowledge into social media data.
Outcome:	The proposed framework can be used to distinguish depression from transient mood changes.

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Dongyu Wang, Jingyu Li, Lan Zhang, null Ganggang.yu, Liang Huang

Programming Languages in NLP

Challenge:	Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics.
Approach:	They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics.
Outcome:	The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics.

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models (2026.acl-long)

Challenge:	Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning.
Approach:	They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database.
Outcome:	The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods.

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Yu Tian, Jie Xing, Ziming Li, Jiang Li, Zehua Duo, Tian Lan, Xu Liu, Guanglai Gao, Xiangdong Su

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)

Challenge:	Existing studies suggest that failures of large language models in social contexts are not due to limited linguistic competence, but to inappropriate recognition.
Approach:	They propose a framework that decomposes social adaptation into three orthogonal dimensions and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions.
Outcome:	The proposed framework decomposes social adaptation into three orthogonal dimensions and conducts controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions.

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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

TED-EL: A Corpus for Speech Entity Linking (2024.lrec-main)

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Silin Li, Ruoyu Song, Tianwei Lan, Zeming Liu, Yuhang Guo

Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization (2026.findings-acl)

Challenge:	Current entity linking tasks rely on textual information, but entities usually exist in textual, audio, and visual contexts in real-world data such as social media and video websites.
Approach:	They propose a speech entity linking task to recognize mentions from speech and link them to entities in knowledge bases.
Outcome:	The proposed model outperforms the existing models on the TED-EL dataset, scoring an F1 score of 60.68%.

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Yu Li, Sizhe Tang, Tian Lan

Reasoning

Challenge:	Existing approaches to reinforcement learning for Large Language Models treat trajectories as independent chains and ignore critical steps that may disproportionally impact reasoning outcome.
Approach:	They propose a framework that recovers latent correlated reward structure across seemingly independent trajectories by identifying and merging functionally similar steps/nodes.
Outcome:	The proposed framework recovers latent correlated reward structure across seemingly independent trajectories.

Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis (2024.findings-emnlp)

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Jianxiang Yu, Zichen Ding, Jiaqi Tan, Kangyang Luo, Zhenmin Weng, Chenghua Gong, Long Zeng, RenJing Cui, Chengcheng Han, Qiushi Sun, Zhiyong Wu, Yunshi Lan, Xiang Li

Table-as-Search: Agentic Information Seeking is Table Completion (2026.findings-acl)

Challenge:	Existing approaches to review scientific papers are limited by their content or quality . SEA is a framework for automated scientific review, but its contents are generic or partial.
Approach:	They propose a framework for automated scientific review using large language models . they propose to use a standardized review dataset to fine-tune an LLM to generate high-quality reviews.
Outcome:	The proposed framework can generate high-quality reviews from standardized datasets and improves on the existing feedback mechanisms.

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Tian Lan, Felix Henry, Bin Zhu, Qianghuai Jia, Junyang Ren, Qihang PU, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo

Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)

Challenge:	Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states within one plain-text context is inherently fragile.
Approach:	They propose a structured planning framework that reformulates the InfoSeeking task as a Table Completion task.
Outcome:	The proposed framework outperforms state-of-the-art frameworks across three kinds of benchmarks, including multi-agent framework and commercial systems.

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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai

Syntactic Text Processing

Challenge:	Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors.
Approach:	They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability.
Outcome:	The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections.

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity (2025.findings-acl)

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Zhibin Lan, Liqiang Niu, Fandong Meng, Wenbo Li, Jie Zhou, Jinsong Su

Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)

Challenge:	Existing large multimodal models typically divide high-resolution images into multiple local images and a global image, leading to a large number of visual tokens.
Approach:	They propose an LMM that can adaptively select the appropriate visual granularity based on the input image and instruction.
Outcome:	The proposed model significantly reduces visual tokens and speeds up inference on 11 benchmarks.

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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao

NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)

Challenge:	Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding.
Approach:	They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss .
Outcome:	The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss.

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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan

McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas.
Approach:	They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights.
Outcome:	The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.

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Tian Lan, Xiangdong Su, Xu Liu, Ruirui Wang, Ke Chang, Jiang Li, Guanglai Gao

Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)

Challenge:	Existing datasets on bias evaluation for large language models focus on English and North American culture and are limited to one task.
Approach:	They propose to evaluate Chinese language models' biases from multiple perspectives using a multi-task Chinese Bias Evaluation Benchmark.
Outcome:	The proposed model covers 12, 82 subcategories and 5 evaluation tasks covering a wide range of categories and content diversity.

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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv

Text Generation

Challenge:	Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build.
Approach:	They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.
Outcome:	The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence.

F²Bench: An Open-ended Fairness Evaluation Benchmark for LLMs with Factuality Considerations (2025.emnlp-main)

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Tian Lan, Jiang Li, Yemin Wang, Xu Liu, Xiangdong Su, Guanglai Gao

CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)

Challenge:	Existing fairness evaluation benchmarks for large language models rely on closed-ended evaluation formats that overlook factuality considerations rooted in historical, social, physiological, and cultural contexts.
Approach:	They propose an open-ended fairness evaluation benchmark for large language models . they incorporate factuality considerations and multi-turn reasoning into the benchmark .
Outcome:	The proposed benchmark incorporates factual grounding and text generation to better reflect the complexities of real-world model usage.

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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan

Challenge:	Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages .
Approach:	They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models .
Outcome:	The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English .

LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)

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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan

Attention Consistency for LLMs Explanation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity.
Approach:	They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples .
Outcome:	The proposed method significantly reduces the size of training data while maximizing the submodular gain.

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Tian Lan, Jinyuan Xu, Xue He, Jenq-Neng Hwang, Lei Li

Explainability & Interpretability in NLP

Challenge:	Existing interpretability methods face limitations such as low resolution and high computational cost.
Approach:	They propose a multi-layer attention consistency score to estimate the importance of input tokens in large language models.
Outcome:	The proposed heuristic achieves a favorable trade-off between interpretability quality and computational efficiency .

TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation (2026.findings-acl)

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Wenbiao Tao, Xinyuan Li, Yunshi Lan, Weining Qian

ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval (2026.acl-long)

Challenge:	Existing approaches to retrieval-augmented generation rely on fragment-level retrieval . GraphRAG suffers from inefficiencies in information extraction and costly resource consumption .
Approach:	They propose a tag-guided hierarchical knowledge graph RAG framework for efficient global reasoning and scalable graph maintenance.
Outcome:	GraphRAG achieves an average win rate of 78.36% on a dataset spanning agriculture, computer science, law, and cross-domain settings compared with baselines .

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Jianlyu Chen, Junwei Lan, Chaofan Li, Defu Lian, Zheng Liu

Representation Learning

Challenge:	Recent studies suggest that traditional retrievers struggle with reasoningintensive tasks such as personal assistants and scientific research.
Approach:	They propose a new data synthesis method that overcomes the triviality problem prevalent in previous synthetic datasets and propose 'ReMixer', a data fusion method that generates 82K high-quality training samples.
Outcome:	The proposed model outperforms existing models on reasoning-intensive retrieval tasks.

Understanding Client Reactions in Online Mental Health Counseling (2023.acl-long)

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Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang, Huachuan Qiu, Zhenzhong Lan

CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning (2026.findings-acl)

Challenge:	Communication success relies heavily on reading participants’ reactions, but little research is on how listeners' reactions shape trajectories and outcomes of conversations.
Approach:	They propose to use client reactions to predict counseling outcomes by using an annotation framework that encompasses counselors’ strategies and client reaction behaviors.
Outcome:	The proposed framework can predict counselors' strategies and client reaction behaviors against a large-scale text-based counseling dataset.

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Yangsong Lan, Hongliang Dai, Piji Li

AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment (2025.emnlp-industry)

Challenge:	Existing approaches to compress CoT with external compressors fail to align with the model’s internal reasoning dynamics, resulting in the loss of critical logical steps.
Approach:	They propose a framework that exploits the model’s intrinsic saliency to compress CoT by exploiting its reasoning termination token .
Outcome:	The proposed framework reduces redundancy in reasoning chain by exploiting the model’s intrinsic saliency.

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Xiaochong Lan, Jie Feng, Yinxing Liu, null Xinleishi, Yong Li

RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2024.findings-naacl)

Challenge:	Existing methods for assessing review quality are unscalable across domains and fail to adapt to evolving content patterns.
Approach:	They propose an LLM-based agent framework that automates the discovery of interpretable features.
Outcome:	The proposed framework improves on a large-scale online platform with a billion-level user base.

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Saeed Khaki, JinJin Li, Lan Ma, Liu Yang, Prathap Ramachandra

Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents (2023.findings-acl)

Challenge:	Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent.
Approach:	They propose to combine rejection sampling and direct preference optimization to improve alignment with user intent by identifying pairs of contrastive samples from human annotator and alternative LLMs.
Outcome:	The proposed method outperforms existing methods including RS, PPO, and DPO in a limited resource environment.

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Haowei Du, Yansong Feng, Chen Li, Yang Li, Yunshi Lan, Dongyan Zhao

Structured Data in NLP

Challenge:	Existing approaches to conditional question answering on long documents ignore document structure and discourse relations between sentences in document sections.
Approach:	They construct a Structure-Discourse Hierarchical Graph and conduct bottom-up information propagation to address this issue.
Outcome:	The proposed approach outperforms the existing methods on the conditional question answering on long documents by 3.0 EM score and 2.4 F1 score on answer measuring, and 2.2 EM and 1.9 F1 scores on jointly answer and condition measuring.

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)

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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao

Machine Translation

Challenge:	Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding.
Approach:	They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language.
Outcome:	The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language.

COCOGEC: Counterfactual Generation for Robust Grammatical Error Correction (2026.findings-acl)

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Qianyu Wang, Xiaoman Wang, Yuanyuan Liang, Xinyuan Li, Yunshi Lan

Robustness in NLP

Challenge:	Existing GEC models fail to understand error patterns in varying contexts . a framework that generates copies of training instances with error-irrelevant contexts altered is proposed .
Approach:	They propose a framework that generates copies of training instances with error-irrelevant contexts altered.
Outcome:	The proposed framework outperforms baselines on the simulated tasks and outperformed existing models.

Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)

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Shuhuan Gu, Wenbiao Tao, Xinchen Ma, Kangkang He, Ye Guo, Xiang Li, Yunshi Lan

Low-Resource NLP

Challenge:	Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs.
Approach:	They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels.
Outcome:	The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity.

PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models (2025.findings-emnlp)

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Wanru Zhuang, Wenbo Li, Zhibin Lan, Xu Han, Peng Li, Jinsong Su

Multilinguality

Challenge:	Current TIMT studies focus on providing translations for all text within an image, neglecting to provide bounding boxes and covering limited scenarios.
Approach:	They extend traditional TIMT into position-aware TIMt to support fine-grained translation . they introduce an Adaptive Image OCR Refinement Pipeline to refine results .
Outcome:	The proposed model supports fine-grained and layout-preserving translation . the experimental data highlight the scalability and generalizability of the model.

Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)

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Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan

Low-Resource NLP

Challenge:	Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations.
Approach:	They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch.
Outcome:	The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance.

R3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context (2023.findings-emnlp)

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Qingyuan Tian, Hanlun Zhu, Lei Wang, Yang Li, Yunshi Lan

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	Existing studies have evaluated LLMs under noise-free context but the dilemma for LLM to produce inaccurate results under noisy context has not been fully investigated.
Approach:	They propose a new method for CoT reasoning using Chain-of-Thought prompting that interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction.
Outcome:	The proposed method outperforms existing CoT prompting methods on five reasoning tasks under noisy context.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training (2024.emnlp-main)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Wenbo Li, Guohao Li, Zhibin Lan, Xue Xu, Wanru Zhuang, Jiachen Liu, Xinyan Xiao, Jinsong Su

Visual Data in NLP

Challenge:	Existing text-to-image models struggle to generate images with legible visual texts . current models lack support for Chinese texts, misspelling, and lack of diversity .
Approach:	They propose to empower backbone models to generate visual texts in Chinese and English . they propose to augment conventional training objective with glyph-aware training losses .
Outcome:	The proposed methods can generate visual texts in English and Chinese while maintaining image generation quality.

Open-Set Living Need Prediction with Large Language Models (2025.findings-acl)

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Xiaochong Lan, Jie Feng, Yizhou Sun, Chen Gao, Jiahuan Lei, Xinleishi Xinleishi, Hengliang Luo, Yong Li

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

Challenge:	Existing approaches to living need prediction treat it as a closed-set classification problem, severely limiting their ability to capture diversity and complexity of living needs.
Approach:	They propose a system leveraging large language models for unrestricted need prediction that leverages Maslow's hierarchy of needs to align predictions with human living needs.
Outcome:	The proposed system outperforms closed-set approaches on need-based life service recall by an average of 19.37% on real-world datasets.

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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng

Natural Language Interfaces

Challenge:	Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings.
Approach:	They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation.
Outcome:	The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression.

Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs (2025.findings-emnlp)

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Yanhong Li, Zixuan Lan, Jiawei Zhou

Green & Sustainable NLP

Challenge:	visual text representations are a practical and surprisingly effective form of input compression for decoder LLMs.
Approach:	They exploit visual representations to render long text inputs as a single image and provide it directly to the model.
Outcome:	The proposed method reduces token usage while preserving performance.

Initializing and Retrofitting Key-Value Adaptors for Traceable Model Editing (2025.findings-acl)

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Hanlun Zhu, Yunshi Lan, Xiang Li, Weining Qian

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

Challenge:	Language models (LMs) are becoming imperative tools for consulting in realworld scenarios.
Approach:	They propose a model editing method that initializes and retrofits key-value pairs into MLP blocks to construct a new mapping of a piece of knowledge without damaging irrelevant knowledge.
Outcome:	The proposed method outperforms baseline methods on a series of GPT series models on edit success and generalization without influencing specificity.

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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

History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling (2023.acl-long)

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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Hao Sun, Yang Li, Liwei Deng, Bowen Li, Binyuan Hui, Binhua Li, Yunshi Lan, Yan Zhang, Yongbin Li

Natural Language Interfaces

Challenge:	Existing methods for conversational KBQA assume the independence of utterances and model them in isolation.
Approach:	They propose a History Semantic Graph Enhanced KBQA model that models long-range semantic dependencies in conversation history while maintaining low computational cost.
Outcome:	The proposed model outperforms baselines on a widely used question type dataset.

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark (2025.findings-emnlp)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Lan Li, Liri Fang, Bertram Ludäscher, Vetle I Torvik

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)

Challenge:	Data cleaning is a time-consuming and error-prone manual process even with modern workflow tools like OpenRefine.
Approach:	AutoDCWorkflow generates a table with a data analysis purpose and generates an open-refine workflow.
Outcome:	The proposed pipeline generates clean, minimal tables for data analysis tasks.

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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen

CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit (2026.acl-long)

Challenge:	Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer.
Approach:	They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs).
Outcome:	The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation.

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Kangyu Wang, Zhiyun Jiang, Haibo Feng, Weijia Zhao, Lin Liu, Jianguo Li, Zhenzhong Lan, Weiyao Lin

Learning to Select In-Context Demonstration Preferred by Large Language Model (2025.findings-acl)

Challenge:	Diffusion large language models generate text through iterative denoising with bidirectional attention, enabling richer contextual dependencies.
Approach:	They propose a training-free parallel decoding method that fuses Trace Credit with current logits to boost the confidence of correct but underconfident tokens.
Outcome:	The proposed method achieves 5.48 times speedup with +0.48 accuracy on LLaDA-8B and is orthogonal to mainstream inference optimizations.

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Zheng Zhang, Shaocheng Lan, Lei Song, Jiang Bian, Yexin Li, Kan Ren

CEDAR: A Chinese Evaluation Dataset for Computational Argumentation (2026.acl-long)

Challenge:	In-context learning (ICL) enables large language models to perform tasks with only a few examples as demonstrations.
Approach:	They propose a generative preference learning framework that leverages LLM feedback to directly optimize demonstration selection for ICL.
Outcome:	Experiments on 19 datasets across 11 task categories show that GenICL achieves superior performance than existing methods in selecting the most effective demonstrations.

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Tian Lan, Jiang Li, Rong Yan, Feilong Bao, Weihua Wang, Guanglai Gao, Xiangdong Su

Reasoning

Challenge:	Existing debate datasets neglect important labels for argument mining, generation, and evaluation.
Approach:	They propose a Chinese Evaluation Dataset for Computational Argumentation that includes key arguments and key rhetorical figures, debater roles, modal words, debate results and transcripts.
Outcome:	The proposed dataset covers 600 debates about 318 topics from Chinese debate competitions.

HSCodeComp: A Realistic and Expert-level Agent Benchmark for Hierarchical Rule Application (2026.acl-long)

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Tian Lan, Yiqian Yang, Qianghuai Jia, Li Zhu, Hui Jiang, Hang Zhu, Weihua Luo, Longyue Wang

Reasoning

Challenge:	Existing agent benchmarks neglect hierarchical rule application in real-world domains . a critical gap persists in numerous real-life professional domains where decision-making is governed by expert-written rules.
Approach:	They propose a benchmark requiring agents to assign a unique 10-digit Harmonized System (HS) Code to products by aligning their fuzzy attributes with strict tariff classification rules.
Outcome:	The proposed benchmarks lack hierarchical rule application capability in real-world domains . the proposed benchmark is based on e-commerce and is open-source .

ROSE: Robust Selective Fine-tuning for Pre-trained Language Models (2022.emnlp-main)

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Lan Jiang, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Rui Jiang

Robustness in NLP

Challenge:	Recent studies have highlighted the lack of adversarial robustness in pre-trained models.
Approach:	They propose a fine-tuning approach that conducts selective updates when adapting pre-trained models to downstream tasks.
Outcome:	The proposed approach improves adversarial robustness on downstream tasks . it eliminates spurious updates, leading to flatter and wider optima than the conventional method .

Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music (2026.acl-long)

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Hongju Su, Ke Li, Lan Yang, Honggang Zhang, Yi-Zhe Song

Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)

Challenge:	Existing symbolic music generation models represent musical notes as a sequence of attribute tokens with fixed unidirectional dependencies.
Approach:	They propose a symbolic music generation framework that adopts a autoregressive and a discrete diffusion architectures for note attributes.
Outcome:	The proposed framework improves state-of-the-art models across objective and subjective metrics.

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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen

Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)

Challenge:	Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate.
Approach:	They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation.
Outcome:	The proposed method outperforms the state-of-the-art by 1.20% on four public datasets.

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Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark (2026.findings-acl)

Challenge:	Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition.
Approach:	They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation.
Outcome:	The proposed model can associate the image with relevant texts, providing useful supplementary information for translation.

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Ziming Li, Yu Tian, Tian Lan, Jiang Li, Zehua Duo, Guanglai Gao, Xiangdong Su

Discourse & Pragmatics

Challenge:	Large language models excel at capturing communicative intent, but they have a side effect: pragmatic hallucination.
Approach:	They propose a benchmark to quantify the impact of pragmatic hallucination on large language models . they propose RLHF and SFT to induce a strong tendency for pragmatic over-attribution .
Outcome:	The proposed model outperforms existing models in predicting pragmatic hallucinations . the evaluations show that current alignment paradigms lack precise control over pragmatic boundaries .

EMA: An Episodic Memory Agent for Efficient and Selective Memory (2026.findings-acl)

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Hongyi Lan, Jiaqi Song, Zhengjia Zhong, Hui Li, Hong Liu, Xianming Lin, Rongrong Ji

Dialogue Systems & Conversational Agents

Challenge:	Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency.
Approach:	They propose a framework that abstracts conversational context into Episodic Memory Units (EMUs) they propose EMA, MemDecider and a filtering decision module to reduce noise and improve overall performance.
Outcome:	The proposed framework reduces token consumption by 11.48% while improving performance on two widely-used benchmarks.

Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs (2024.findings-emnlp)

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Anqi Li, Yu Lu, Nirui Song, Shuai Zhang, Lizhi Ma, Zhenzhong Lan

Challenge:	In traditional face-to-face therapy, the assessment of therapeutic alliance is not directly translated to text-based settings.
Approach:	They propose an automatic approach to understand the development of therapeutic alliance in text-based counseling by using large language models.
Outcome:	The proposed approach demonstrates that the framework is effective in identifying the therapeutic alliance in text-based counseling.

Learning Continuous Temporal Dynamics on Symplectic Manifolds for Temporal Knowledge Graph Embedding (2026.findings-acl)

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Jiang Li, Zehua Duo, Tian Lan, Feilong Bao, Guanglai Gao, Xiangdong Su

Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)

Challenge:	Existing methods for temporal knowledge graph embedding lack explicit structural constraints for continuous-time dynamics.
Approach:	They propose a Temporal Knowledge Graph Embedding framework that embeds temporal dynamics into a symplectic phase space.
Outcome:	The proposed framework achieves competitive performance with lower embedding dimensions.

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Jiang Li, Pengfei Cao, Chenxi Zhou, Tian Lan, Xiangdong Su, Kang Liu, Jun Zhao, Guanglai Gao

Challenge:	Large language models (LLMs) have impressive performance but require computational and memory resources.
Approach:	They propose a post-training framework that uses a Haar wavelet transform to prune weights.
Outcome:	The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture.

ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model (2026.findings-acl)

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Ruirui Wang, Haoran Zhang, Tian Lan, Zehua Duo, Jiang Li, Guanglai Gao, Xiangdong Su

Linguistics & Cognitive NLP

Challenge:	Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations fail to capture the infer–apply loop that arises in real-world dialogue.
Approach:	They propose a benchmark that conditions on the audience persona p and the Elaboration Likelihood Model (ELM) route r within persuasive conversations.
Outcome:	The proposed model can model the interlocutor's mental states over multiple turns and adapt strategy and tone accordingly.

A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)

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Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan

StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)

Challenge:	Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings.
Approach:	They propose a method to generate unsupervised alignment seeds using temporal information from TKGs.
Outcome:	The proposed method outperforms the previous methods by using temporal information.

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Xiaoming Liu, Chen Liu, Zhaohan Zhang, Chengzhengxu Li, Longtian Wang, Yu Lan, Chao Shen

RAISE: Reinforced Adaptive Instruction Selection For Large Language Models (2025.findings-emnlp)

Challenge:	Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting.
Approach:	They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization.
Outcome:	Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average.

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Qingsong Lv, Yangning Li, Zihua Lan, Zishan Xu, Jiwei Tang, Tingwei Lu, Yinghui Li, Wenhao Jiang, Hong-Gee Kim, Hai-Tao Zheng, Philip S. Yu

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Existing selection methods rely on static, heuristic quality scores and are executed only once before training.
Approach:	They propose a dynamic selection framework that integrates selection into every training step.
Outcome:	The proposed framework integrates selection into every training step.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen