Papers by Lu Xiang

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)

Challenge:	Existing linguistic knowledge bases such as URIEL+ lack a principled method for aggregating these signals into a single, comprehensive score.
Approach:	They propose a framework for type-matched language distances that unifies these signals into a robust, task-agnostic composite distance.
Outcome:	The proposed representations improve transfer performance when the distance type is relevant to the task, while yielding gains in most tasks.

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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi

Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)

Challenge:	Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited.
Approach:	They propose an inference-time policy adapter which tailors a large base model without fine-tuning it.
Outcome:	The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4.

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Xingyu Lu, Yumeng Ma, Xiang Zhou, Shengli Gan, Guiying Deng, Yang Wen, Yanbing Liu

Sentiment Analysis

Challenge:	Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings.
Approach:	They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions.
Outcome:	The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset.

Demystifying Small Language Models for Edge Deployment (2025.acl-long)

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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things.
Approach:	They propose to use SLMs to build and optimize a set of small language models that are publicly accessible.
Outcome:	The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu, Chu-Ren Huang

Sentiment Analysis

Challenge:	Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated.
Approach:	They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models.
Outcome:	The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform.

Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings (2020.starsem-1)

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Emmanuele Chersoni, Rong Xiang, Qin Lu, Chu-Ren Huang

Representation Learning

Challenge:	Normative studies on modality for English words are relatively common . however, they are limited to a relatively small number of languages and require costly ratings.
Approach:	They aim to learn a mapping between word embeddings and modality norms by training on a high-resource language and testing on . monolingual and crosslingual word embeds are used to predict modality association scores .
Outcome:	The proposed model predicts modality associations even when trained on an English resource and tested on a completely unseen language.

Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)

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Jie Sun, Junkang Wu, Jiancan Wu, Zhibo Zhu, Xingyu Lu, Jun Zhou, Lintao Ma, Xiang Wang

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

Challenge:	Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications.
Approach:	They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level.
Outcome:	The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance.

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

ToM-Synth: Scaling Robust Theory of Mind in LLMs via 6,912 Structured Social Units (2026.findings-acl)

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

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Guiyang Hou, Xiang Huang, Shangke Lyu, Yuchuan Wu, Weiyao Luo, Xinyu Mei, Yongliang Shen, Weiming Lu, Yongbin Li

Robustness in NLP

Challenge:	Existing methods endowing LLMs with Theory of Mind fail to internalize the augmented ToM into the LLM.
Approach:	They propose a factorial combinatorial synthesis framework that enables systematic synthesis of ToM data and uses it for RL fine-tuning.
Outcome:	The proposed framework yields a training dataset of 27,648 instances.

LayoutDIT: Layout-Aware End-to-End Document Image Translation with Multi-Step Conductive Decoder (2023.findings-emnlp)

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	Existing methods struggle to capture the visual layout in complex document images.
Approach:	They propose to integrate layout knowledge into document image translation by using a layout-aware encoder and a multi-step conductive decoder to achieve the translation step by step.
Outcome:	The proposed model outperforms state-of-the-art methods with better parameter efficiency.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

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

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

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

EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User’s Internal World (2026.acl-long)

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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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Natural Language Interfaces

Challenge:	EmoHarbor is an evaluation framework that rewards generic empathetic responses but fails to assess whether the support is genuinely personalized to users’ unique psychological profiles and contextual needs.
Approach:	They propose an automated evaluation framework that adopts a User-as-a-Judge paradigm by simulating the user's inner world.
Outcome:	The proposed framework decomposes users' internal processes into three specialized roles and defines 10 evaluation dimensions of personalized support quality.

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)

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Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Challenge:	Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch.
Approach:	They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks.
Outcome:	The proposed method significantly outperforms baseline models on translation tasks and handling the entities.

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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

Reasoning

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025.findings-emnlp)

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Challenge:	Large Language Models (LLMs) show great potential for expressing empathy, but often deliver generic responses that fail to address users’ specific needs.
Approach:	They propose a self-evolution framework to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context.
Outcome:	The proposed model significantly improves the model's performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.

A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)

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Jinghui Yan, Yining Wang, Lu Xiang, Yu Zhou, Chengqing Zong

Challenge:	Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases.
Approach:	They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining.
Outcome:	The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures.

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)

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An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang

Robustness in NLP

Challenge:	Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world .
Approach:	They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions.
Outcome:	The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents.

Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)

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Wei Xiang, Bang Wang, Lu Dai, Yijun Mo

Challenge:	Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR).
Approach:	They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network.
Outcome:	The proposed model achieves state-of-the-art on the PDTB 3.0 corpus.

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent (2025.coling-main)

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions, but their responses are often verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios.
Approach:	They propose a strategy-enhanced role-playing framework that emulates real-world interactions and a dataset that is used to develop an emotional support agent.
Outcome:	The proposed framework emulates real-world interactions and promotes a broader range of dialogues and Emotional Support Agent training.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge (D19-55)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Wenhao Ying, Rong Xiang, Qin Lu

Information Retrieval

Challenge:	Text in domains like social media has its own salient characteristics.
Approach:	They propose a method to obtain domain knowledge and integrate it with general knowledge to improve emotion classification.
Outcome:	The proposed method improves performance of emotion classification on Twitter data.

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

Challenge:	Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach:	They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome:	The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.

Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation (2024.acl-long)

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Aiwei Liu, Haoping Bai, Zhiyun Lu, Xiang Kong, Xiaoming Wang, Jiulong Shan, Meng Cao, Lijie Wen

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)

Challenge:	Existing methods to evaluate preference data without human annotations are difficult . et al., 2022b) is effective for aligning large language models with human expectations .
Approach:	They propose a method to evaluate the response preference using output probabilities under contrastive prompts.
Outcome:	The proposed method could surpass the RLHF method without human-annotated preference data.

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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen

DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)

Challenge:	Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge.
Approach:	They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance.
Outcome:	Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points.

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Yuhan Liu, Cong Xu, Lu Liu, Yihua Wang, Feiyu Chen, Qi Jia, Yaqian Zhao, Zhichun Wang, Xiang Li

Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)

Challenge:	Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties.
Approach:	They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning.
Outcome:	The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation.

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Rong Xiang, Yunfei Long, Mingyu Wan, Jinghang Gu, Qin Lu, Chu-Ren Huang

Sentiment Analysis

Challenge:	Existing deep neural network models lack mechanisms to highlight important sentiment terms.
Approach:	They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms.
Outcome:	The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets.

A Query-Response Framework for Whole-Page Complex-Layout Document Image Translation with Relevant Regional Concentration (2025.findings-acl)

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Zhiyuan Chen, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

Machine Translation

Challenge:	Existing methods for document image translation rely on the vanilla encoder-decoder paradigm . a novel dynamic aggregation mechanism is designed to enhance the text semantics in query features toward translation.
Approach:	They propose a Query-Response DIT framework that reformulates the DIT task into a parallel response/translation process of multiple queries.
Outcome:	The proposed framework improves translation quality on four translation directions on three benchmarks.

Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models (2024.lrec-main)

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Zhuoqun Li, Hongyu Lin, Yaojie Lu, Hao Xiang, Xianpei Han, Le Sun

MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)

Challenge:	Recent advances in large language models have push NLP into a new era, moving away from traditional task-specific pre-train finetuning paradigm.
Approach:	They provide a comprehensive analysis of declarative and procedural knowledge for large language models and evaluate their effectiveness.
Outcome:	The proposed model can perform better with both kinds of knowledge, but at different speeds.

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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou

Reasoning

Challenge:	In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective.
Approach:	They propose to fine-tune data augmentation by query evolution and diverse reasoning paths.
Outcome:	The proposed model achieves new state-of-the-art on GSM8K and MATH.

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation (2025.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Yupu Liang, Yaping Zhang, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models (2025.findings-acl)

Challenge:	Document Image Machine Translation (DIMT) faces generalization challenges due to limited training data and the complex interplay between visual and textual information.
Approach:	They propose a single-to-mix Modality alignment framework leveraging Multimodal Large Language Models (MLLMs) this framework aligns an imageonly encoder with multimodal representations of an MLLM pre-trained on large-scale document image datasets.
Outcome:	The proposed framework improves translation quality in cross-domain generalization and challenging document image scenarios.

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Hao Xiang, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Ben He, Le Sun, Jingren Zhou, Junyang Lin

Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)

Challenge:	Prior research focused on developing data generation methods, while insufficient attention has been paid to quality control mechanisms and often produces inaccurate and unhelpful data.
Approach:	They propose an algorithm that automatically generates high-quality preference data, eliminating manual annotation requirements.
Outcome:	The proposed algorithm outperforms baselines in human preference alignment and reward optimization.

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Yupu Liang, Yaping Zhang, Cong Ma, Zhiyang Zhang, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)

Challenge:	Existing TIMT tasks focus on text-line-level images.
Approach:	They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation.
Outcome:	The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation.

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Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)

Challenge:	Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse.
Approach:	They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints.
Outcome:	The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints .

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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention.
Approach:	They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction.
Outcome:	The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li

Challenge:	Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime .
Approach:	They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM .
Outcome:	Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets .

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)

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Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)

Challenge:	Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information.
Approach:	They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information.
Outcome:	The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets.

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Lu Dai, Bang Wang, Wei Xiang, Yijun Mo

Improving MLLM’s Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency (2025.findings-acl)

Challenge:	Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments.
Approach:	They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models.
Outcome:	The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset.

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Yupu Liang, Yaping Zhang, Zhiyang Zhang, Zhiyuan Chen, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou

Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025.naacl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges.
Approach:	They propose a novel fine-tuning paradigm that allows the model to generate OCR text before producing translation text, which allows it to leverage its strong monolingual OCR ability while learning to translate text across languages.
Outcome:	The proposed model can leverage its strong monolingual OCR ability while learning to translate text across languages.

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Ang Li, Yiquan Wu, Ming Cai, Adam Jatowt, Xiang Zhou, Weiming Lu, Changlong Sun, Fei Wu, Kun Kuang

Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)

Challenge:	Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case.
Approach:	They propose a knowledge-enhanced approach that incorporates 'label-level knowledge' to enhance the representation of case facts for each task and 'task-level' knowledge to improve synergy.
Outcome:	The proposed method is effective in comparison to state-of-the-art (SOTA) baselines.

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Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang

Challenge:	Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences.
Approach:	They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences.
Outcome:	Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs.

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022.findings-emnlp)

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Nuo Chen, Qiushi Sun, Renyu Zhu, Xiang Li, Xuesong Lu, Ming Gao

Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)

Challenge:	Existing code pre-trained models fail to consider inherent characteristics of codes . Existing methods to interpret code pretrained model fail to take into account inherent characteristics .
Approach:	They propose a probing method to quantitatively interpret how CodePTMs attend code structure.
Outcome:	The proposed method denoises input code sequences and measures commonality between token-level attention scores and pair-wise distances between corresponding AST nodes.

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Zili Wang, Tianyu Zhang, Haoli Bai, Lu Hou, Xianzhi Yu, Wulong Liu, Shiming Xiang, Lei Zhu

Challenge:	Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective.
Approach:	They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism.
Outcome:	The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches .

From Chaotic OCR Words to Coherent Document: A Fine-to-Coarse Zoom-Out Network for Complex-Layout Document Image Translation (2025.coling-main)

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Zhiyang Zhang, Yaping Zhang, Yupu Liang, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong

ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition (2022.coling-1)

Challenge:	Document Image Translation (DIT) aims to translate documents in images from one language to another.
Approach:	They propose a novel end-to-end network called Zoom-out DIT to improve document translation by combining word positioning, sentence recognition and document organization.
Outcome:	The proposed network improves word positioning, sentence recognition and document organization, and improves translation quality.

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Wei Xiang, Zhenglin Wang, Lu Dai, Bang Wang

Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)

Challenge:	Existing paradigms for Implicit Discourse Relation Recognition (IDRR) do not exploit linguistic evidence embedded in the pre-training process.
Approach:	They propose a new paradigm to detect and classify relation sense between two text segments without an explicit connective.
Outcome:	The proposed method significantly outperforms the state-of-the-art algorithms even with fewer training data.

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Xiang Cheng, Yulan Hu, Xiangwen Zhang, Lu Xu, Lide Tan, Zheng Pan, Xin Li, Yong Liu

Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization (2025.findings-naacl)

Challenge:	Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries.
Approach:	They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios.
Outcome:	The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities.

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Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia, Xiang Kong, Qi Zhu, Xiaoming Simon Wang, Oncel Tuzel, Raviteja Vemulapalli

Natural Language Interfaces

Challenge:	Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings.
Approach:	They propose Mutual Reinforcing Data Synthesis (MRDS) within large language models to enhance few-shot dialogue summarization task.
Outcome:	Empirical results show that the proposed method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings.

In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search (2024.emnlp-main)

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Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren

Information Retrieval

Challenge:	Logic-Induced-Knowledge-Search (LINK) is a framework for generating factually-correct yet long-tail inferential knowledge.
Approach:	They introduce a framework to obtain factually-correct yet long-tail inferential statements using variable-wise prompting grounded on symbolic rules.
Outcome:	The proposed framework is able to obtain factually-correct yet long-tail inferential statements while ensuring factual correctness.

Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource (2020.aacl-main)

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Rong Xiang, Mingyu Wan, Qi Su, Chu-Ren Huang, Qin Lu

Challenge:	Mandarin Alphabetical Words (MAWs) are a key component of Modern Chinese . they are characterized by unique code-mixing idiosyncrasies influenced by language exchanges .
Approach:	They propose to construct a large collection of Mandarin Alphabetic Words from Sina Weibo . they propose to use a web-based technique to identify and validate MAWs .
Outcome:	The proposed method identifies 16,207 Mandarin Alphabetic Words (MAWs) using a web-based technique . the results show that the proposed method is useful for linguistic research and inquiries .

SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)

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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

Challenge:	Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences.
Approach:	They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context.
Outcome:	The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs.

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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang

Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)

Challenge:	Current document image parsing solutions rely on specialized models or generate content autoregressively.
Approach:	They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation.
Outcome:	The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency.

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Jiateng Wei, Quan Lu, Ning Jiang, Siqi Li, Jingyang Xiang, Jun Chen, Yong Liu