Papers by Feng Luo

Rethinking Diverse Human Preference Learning through Principal Component Analysis (2025.findings-acl)

Challenge:	Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap .
Approach:	They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models.
Outcome:	The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting .

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Feng Luo, Rui Yang, Hao Sun, Chunyuan Deng, Jiarui Yao, Jingyan Shen, Huan Zhang, Hanjie Chen

Explainability & Interpretability in NLP

Challenge:	Decomposed Reward Models extract diverse human preferences from binary comparisons without fine-grained annotations.
Approach:	They propose a decomposed reward model that extracts diverse human preferences from binary comparisons without fine-grained annotations.
Outcome:	The proposed approach extracts diverse human preferences from binary comparisons without fine-grained annotations.

Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)

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Bo Xu, Longjiao Li, Wei Luo, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia

Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)

Challenge:	Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers.
Approach:	They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations.
Outcome:	The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models.

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Mingqi Li, Fei Ding, Dan Zhang, Long Cheng, Hongxin Hu, Feng Luo

ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice (2024.acl-demos)

Challenge:	Existing methods for improving multilingual models did not focus on learning the semantic structure of representation.
Approach:	They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives .
Outcome:	The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show .

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Yutong Hu, Kangcheng Luo, Yansong Feng

Explainability & Interpretability in NLP

Challenge:	Large Language Models (LLMs) have shown impressive performance in various tasks, showing great potential for specific domains, such as law (Lai et al., 2023), finance (Zeng e e al. 2023) and law (Lam elms, 2024).
Approach:	They propose to use large language models to provide interpretable, accurate, and informative legal advice by visually presenting the correlation between legal articles and LLM's response by calculating their similarities.
Outcome:	The proposed model provides users with an intuitive legal basis for the responses and retrieves relevant legal cases for user reference.

LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)

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Yuan Zhang, Wanhong Huang, Yi Feng, Chuanyi Li, Zhiwei Fei, Jidong Ge, Bin Luo, Vincent Ng

Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks (2023.emnlp-main)

Challenge:	Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness.
Approach:	They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights.
Outcome:	Extensive tests reveal weaknesses in LJP models and provide diagnostic insights.

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Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Qinghua Zheng, Minnan Luo

Knowledge Representation

Challenge:	Existing models focus on the textual content of the review, while spoiler detection requires putting the review into the context of facts and knowledge regarding movies.
Approach:	They propose a network-based spoiler detection model that takes into account external knowledge about movies and user activities on movie review platforms.
Outcome:	The proposed model takes into account external knowledge about movies and user activities on movie review platforms while incorporating user networks.

A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)

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He Zhao, Zhunchen Luo, Chong Feng, Anqing Zheng, Xiaopeng Liu

Text Classification

Challenge:	Existing academic search engines cannot detect relevant papers where a resource is mentioned.
Approach:	They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework .
Outcome:	The proposed model achieves the best results on both the classification task and recommendation task.

BotPercent: Estimating Bot Populations in Twitter Communities (2023.findings-emnlp)

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Zhaoxuan Tan, Shangbin Feng, Melanie Sclar, Herun Wan, Minnan Luo, Yejin Choi, Yulia Tsvetkov

Challenge:	Existing approaches to bot detection are agnostic to social environments the bots operate in . however, standard approaches are not a good fit for the social environments they operate in.
Approach:	They propose a method that estimates the percentage of Twitter bots given a community . they use Twitter bot detection datasets and feature-, text-, and graph-based models adjusted to a particular community based on Twitter .
Outcome:	The proposed method achieves state-of-the-art in community-level Twitter bot detection across balanced and imbalanced class distribution settings.

D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)

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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan

Challenge:	Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction.
Approach:	They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process.
Outcome:	The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process .

Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation (2025.acl-long)

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Kangcheng Luo, Quzhe Huang, Cong Jiang, Yansong Feng

Explainability & Interpretability in NLP

Challenge:	a novel framework for automated legal interpretation is proposed to alleviate the burden on legal experts.
Approach:	They propose a framework for automated legal interpretation that uses large language models to extract concept-related information and interpret legal concepts.
Outcome:	The proposed framework eliminates the need for legal experts to interpret legal concepts . it uses large language models to extract concept-related information and interpret legal concept interpretations .

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)

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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (2022.naacl-main)

Challenge:	Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT .
Approach:	They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models.
Outcome:	The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache.

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Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang

Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues (2026.findings-acl)

Challenge:	Despite advances in self-supervised learning, there is a lack of models that can effectively capture both intra- and intra-item semantics for semi-structured session data.
Approach:	They propose a graph-based transformer model for semi-structured session data that captures both intra- and intra-item semantics.
Outcome:	The proposed model outperforms baselines in three session search and entity linking tasks by up to 9%.

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Zory Zhang, Pinyuan Feng, Bingyang Wang, Tianwei Zhao, Suyang Yu, Qingying Gao, Hokin Deng, Ziqiao Ma, Yijiang Li, Dezhi Luo

Natural Language Interfaces

Challenge:	Where someone looks is a nonverbal communication cue that children and adults readily use.
Approach:	They used 1,360 real-world photos to construct evaluation stimuli for Vision-Language Models (VLMs) they found a substantial performance gap between VLMs and humans .
Outcome:	The proposed model outperforms existing models in predicting gaze direction using head orientation rather than eye appearance.

MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning (2025.emnlp-main)

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Jingyan Shen, Jiarui Yao, Rui Yang, Yifan Sun, Feng Luo, Rui Pan, Tong Zhang, Han Zhao

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

Challenge:	Existing reward models assume a global reward function, limiting personalization and pluralistic alignment.
Approach:	They propose a framework that leverages binary preference datasets to enhance personalized preference learning.
Outcome:	The proposed framework captures diverse human preferences without fine-grained annotations and significantly improves personalized preference learning on downstream tasks.

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Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

D2Plan: Dual-Agent Dynamic Global Planning for Complex Retrieval-Augmented Reasoning (2026.acl-long)

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Kangcheng Luo, Tinglang Wu, Yansong Feng

Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)

Challenge:	Recent advances in reinforcement learning (RL) have empowered Large Language Models (LLMs) with the capability to perform autonomous retrieval during reasoning tasks.
Approach:	They propose a "D2Plan" paradigm for retrieval-augmented reasoning that integrates a 'Reasoner' and a'Purifier'
Outcome:	Experiments show that the proposed paradigm improves on QA benchmarks.

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Yi Feng, Ting Wang, Chuanyi Li, Vincent Ng, Jidong Ge, Bin Luo, Yucheng Hu, Xiaopeng Zhang

Information Retrieval

Challenge:	a method for user targeting is developed to identify online users to whom an ad should be targeted.
Approach:	They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models.
Outcome:	The proposed method can increase positive and negative instances of positive training instances on two datasets.

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)

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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Information Extraction & Text Mining

Challenge:	Massive open online courses (MOOCs) are a popular educational platform for advanced research.
Approach:	They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource.
Outcome:	The proposed datasets show that they can facilitate research in MOOCs.

MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)

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Feiyan Liu, Liangzhi Li, Xiaoli Wang, Feng Luo, Chang Liu, Jinsong Su, Yiming Qian

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026.findings-acl)

Challenge:	Effective EHR representations are key to achieving high performance in healthcare applications.
Approach:	They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes.
Outcome:	The proposed model outperforms baseline models on two real clinical datasets in downstream tasks.

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Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu, Jianhua Tao

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Challenge:	Existing RL methods rely on unstructured self-sampling to fit scalar rewards, resulting in inefficient rollouts.
Approach:	They propose a structured template-guided RL framework that augments policy optimization with explicit template guidance.
Outcome:	Experiments show that TemplateRL outperforms GRPO and GRPI by 99% on AIME and 41% on AMC with superior stability on weak models and remarkable cross-domain generalization.

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

‘No’ Matters: Out-of-Distribution Detection in Multimodality Multi-Turn Interactive Dialogue Download PDF (2025.findings-acl)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Rena Wei Gao, Xuetong Wu, Siwen Luo, Caren Han, Feng Liu

Natural Language Interfaces

Challenge:	Out-of-distribution (OOD) detection is essential for multimodal learning systems . a novel scoring framework is proposed to efficiently detect OOD in multi-round long dialogues .
Approach:	They propose a scoring framework that integrates visual language models with a score framework that detects OOD in two key scenarios.
Outcome:	The proposed framework detects OOD in two key scenarios: mismatches between dialogue and image input pair and previously unseen labels.

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang

DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection (2024.findings-acl)

Challenge:	Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context.
Approach:	They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts.
Outcome:	The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context.

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Herun Wan, Shangbin Feng, Zhaoxuan Tan, Heng Wang, Yulia Tsvetkov, Minnan Luo

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

Challenge:	Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles.
Approach:	They propose to integrate large language models into the news pipeline by generating news reactions and generating proxy tasks.
Outcome:	The proposed model outperforms state-of-the-art baselines by 16.8% in macro f1-score on seven datasets with three LLMs.

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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

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

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

LLaMA Pro: Progressive LLaMA with Block Expansion (2024.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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Chengyue Wu, Yukang Gan, Yixiao Ge, Zeyu Lu, Jiahao Wang, Ye Feng, Ying Shan, Ping Luo

What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection (2024.acl-long)

Challenge:	Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance.
Approach:	They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting.
Outcome:	The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics.

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Shangbin Feng, Herun Wan, Ningnan Wang, Zhaoxuan Tan, Minnan Luo, Yulia Tsvetkov

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)

Challenge:	Social media bot detection has always been an arms race between advancements in machine learning and adversarial bot strategies to evade detection.
Approach:	They propose a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities and propose LLM-guided manipulation of user textual and structured information to evade detection.
Outcome:	The proposed framework outperforms state-of-the-art baselines on 1,000 annotated examples while bringing down existing detectors by 29.6% and harming calibration and reliability of bot detection systems.

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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao

RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)

Challenge:	In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation.
Approach:	They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns.
Outcome:	The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy.

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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf

Dialogue Systems & Conversational Agents

Challenge:	Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts.
Approach:	They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability.
Outcome:	The proposed system can understand and remediate norm violations step by step.

BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)

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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media (2022.naacl-main)

Challenge:	Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution.
Approach:	They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process.
Outcome:	The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work.

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Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, Minnan Luo

Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)

Challenge:	Existing approaches focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles.
Approach:	They propose a political perspective detection approach that leverages news text to enable multi-hop knowledge reasoning and incorporates textual cues as paragraph-level labels.
Outcome:	The proposed approach outperforms state-of-the-art methods on two benchmark datasets.

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Fengyuan Liu, Yi Huang, Sichun Luo, Yuqi Wang, Yazheng Yang, Xinye Li, Zefa Hu, Junlan Feng, Qi Liu

PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)

Challenge:	Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio.
Approach:	They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Outcome:	The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search.

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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo

Representation Learning

Challenge:	Existing approaches focus on textual data and voting records to induce political actors' stances.
Approach:	They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances.
Outcome:	The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection.

Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought (2026.findings-eacl)

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Bowen Li, Ziqi Xu, Jing Ren, Renqiang Luo, Xikun Zhang, Xiuzhen Zhang, Yongli Ren, Feng Xia

HoWToBench: Holistic Evaluation for LLM’s Capability in Human-level Writing using Tree of Writing (2026.acl-long)

Challenge:	Existing prompting methods for Large Language Models (LLMs) suffer from excessive token usage and limited generalisability across diverse reasoning tasks.
Approach:	They propose an Adaptive Causal Prompting with Sketch-of-Thought framework that leverages structural causal models to infer the causal effect of a query on its answer.
Outcome:	The proposed framework outperforms existing prompting baselines in terms of accuracy, robustness, and computational efficiency.

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Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan, Irene Zhou, Zikang Wang, Xiaotao Gu, Jie Tang, Hongning Wang, Minlie Huang

Challenge:	Evaluating the writing capabilities of large language models remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics.
Approach:	They propose to model the aggregation weights of sub-features in a tree-structured workflow and propose a Chinese writing benchmark that mitigates biases.
Outcome:	The proposed tree-of-writing (ToW) measures the writing capabilities of large language models (LLMs) in Chinese and shows that it mitigates biases and achieves a 0.93 Pearson correlation with human judgments.

AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)

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Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)

Challenge:	Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions.
Approach:	They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary.
Outcome:	The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy.

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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu

Multilinguality

Challenge:	Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging.
Approach:	They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer .
Outcome:	The proposed model performs on par with or better than substantially larger baselines.

AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)

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Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng, Qi Liu

Programming Languages in NLP

Challenge:	Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias.
Approach:	They propose a diagnostic benchmark for instruction-grounded financial code generation under strict semantic and temporal constraints.
Outcome:	The proposed benchmarks show that the models fail under causal, structural, or functional constraints.

Self-attention-based Graph-of-Thought for Math Problem Solving (2025.findings-acl)

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Ruiqiao Bai, Xue Han, Shuo Lei, Junlan Feng, Yanyan Luo, Chao Deng

MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)

Challenge:	Existing methods for generating reasoning paths in a chain structure are inefficient and non-human-like.
Approach:	They propose a decoding method for a chain-based LLM that constructs a thought graph simultaneously as an LLM inference and generates reasoning steps with a graph-structured self-attention mechanism.
Outcome:	The proposed method improves reasoning accuracy without huge computational over-expensive LLMs and avoids performance degradation issues when the LLM is too small to comprehend complex prompts.

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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu

Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning (2025.emnlp-industry)

Challenge:	Existing systems focus primarily on assessment rather than treatment planning.
Approach:	They propose a framework that structures LLM reasoning to align with real-life workflows.
Outcome:	The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality.

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Ruizhe Chen, Tianze Luo, Zhiting Fan, Heqing Zou, Zhaopeng Feng, Guiyang Xie, Hansheng Zhang, Zhuochen Wang, Zuozhu Liu, Zhang Huaijian

Visual Data in NLP

Challenge:	Existing methods for video temporal grounding suffer from limited temporal awareness and poor generalization.
Approach:	They propose a two-stage training framework that integrates supervised fine-tuning with reinforcement learning to improve both the accuracy and robustness of VTG models.
Outcome:	The proposed training framework outperforms existing models on multiple benchmarks on open-domain and challenging scenarios.

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

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Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria

Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction (2024.lrec-main)

Challenge:	Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs).
Approach:	They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions .
Outcome:	The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals.

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Yuanzhen Luo, Qingyu Zhou, Feng Zhou

Information Extraction & Text Mining

Challenge:	Existing methods for keyphrase extraction lack the ability to utilize keyphrase information, which may result in biased results.
Approach:	They propose a keyphrase extraction task that leverages the supervised Variational Information Bottleneck to guide the text diffusion process for generating enhanced keyphrase representations.
Outcome:	The proposed keyphrase extraction model outperforms existing methods on open domain keyphrase extractor benchmark and scientific domain dataset.

CogCompNLP: Your Swiss Army Knife for NLP (L18-1)

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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth

Challenge:	a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks.
Approach:	They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community .
Outcome:	The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges.

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)

Challenge:	Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness .
Approach:	They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image.
Outcome:	The proposed model outperforms existing models on key downstream tasks.

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Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang

ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN’S PERSONALITIES (2026.findings-acl)

Challenge:	Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge.
Approach:	They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness.
Outcome:	The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision.

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Yanyan Luo, Xue Han, Chunxu Zhao, Ruiqiao Bai, Yaxing Zhang, Qian Hu, Lijun Mei, Junlan Feng