Papers by Hao Lu

SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility (2026.acl-long)

Challenge:	Existing hierarchical topic models are based on Euclidean space, which cannot retain the hierarchically semantic information in the corpus, leading to irrational structure of the generated topics.
Approach:	They propose a novel hierarchical topic model that uses contrastive learning to capture information from documents.
Outcome:	The proposed model performs on topic coherence and topic diversity, and on the rationality of the topic hierarchy.

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Xuyang Zhi, Peilun Zhou, Chengqiang Lu, Hang Lv, Yiwei Liang, Rongyang Zhang, Yan Gao, null Yiwu, Yao Hu, Hongchao Gu, Defu Lian, Hao Wang, Enhong Chen

Challenge:	Large Language Models (LLMs) are shifting the focus from single verifiable tasks toward complex, open-ended real-world scenarios.
Approach:	They propose a framework that automatically adjusts reward weights and data importance to synchronize learning intent with data utility for optimal performance.
Outcome:	The proposed framework improves model capabilities across all domains and scales.

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Challenge:	Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach:	They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome:	The proposed framework can be used to generate presentations with environmental observations.

Unsupervised Learning of Hierarchical Conversation Structure (2022.findings-emnlp)

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Bo-Ru Lu, Yushi Hu, Hao Cheng, Noah A. Smith, Mari Ostendorf

Dialogue Systems & Conversational Agents

Challenge:	Goal-oriented conversations often have sub-dialogue structure, but it can be domain-dependent . Increasingly, language understanding applications involve conversational speech and text .
Approach:	They propose an unsupervised approach to learning hierarchical conversation structure . they use turn and sub-dialogue segment labels to decode the structure based on dialogue acts and subtasks .
Outcome:	The proposed approach improves neural models for three conversation-level understanding tasks.

ALCAP: Alignment-Augmented Music Captioner (2023.emnlp-main)

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Zihao He, Weituo Hao, Wei-Tsung Lu, Changyou Chen, Kristina Lerman, Xuchen Song

ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)

Challenge:	Traditional approaches to music captioning ignore the intricate interplay between the two . however, a comprehensive understanding of music necessitates the integration of both these elements.
Approach:	They propose a method to learn multimodal alignment between audio and lyrics through contrastive learning.
Outcome:	The proposed method achieves new state-of-the-art on two music captioning datasets.

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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)

Challenge:	Existing tools for detecting safety issues in LLMs are expensive and inefficient.
Approach:	They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions.
Outcome:	The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs.

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Hong Chen, Zhenhua Fan, Hao Lu, Alan Yuille, Shu Rong

Information Extraction & Text Mining

Challenge:	Existing methods for coreference resolution are based on word2vec-like representations of entities.
Approach:	They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers .
Outcome:	The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo .

Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)

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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu

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

Challenge:	Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects.
Approach:	They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt.
Outcome:	The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks.

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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

Multilinguality

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.

Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal (2026.findings-acl)

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Shuyang Zhang, Zhixuan Liu, Zhichen Dong, Hao Zhang, Chaochao Lu, Chao Yang

CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)

Challenge:	Recent training-free prompt optimizers treat performance as maximizing a single scalar score and ignore a second signal that the desired style is task dependent.
Approach:	They propose a semantic-entropy-based method that uses task uncertainty to guide prompt optimization by selecting high-entropicy candidates for creative tasks and low-energetic candidates for conservative ones.
Outcome:	The proposed method outperforms baselines on MT-Bench subsets and integrates easily into existing prompt optimizers.

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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)

Challenge:	Existing methods to evaluate large language models are prone to data contamination.
Approach:	They propose a method which parses contaminated data and back-translates it into a candidate set.
Outcome:	The proposed method reduces data contamination and evaluates the LLMs more cleanly.

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Zhifeng Hao, Zhongjie Chen, Junhao Lu, Shengyin Yu, Guimin Hu, Keli Zhang, Ruichu Cai, Boyan Xu

Information Retrieval

Challenge:	Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning.
Approach:	They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI.
Outcome:	The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy.

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

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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

Dialogue Systems & Conversational Agents

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.

A Large Scale Speech Sentiment Corpus (2020.lrec-1)

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Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, James Fan

Improve Dense Passage Retrieval with Entailment Tuning (2024.emnlp-main)

Challenge:	Existing corpus for sentiment analysis uses text inputs, but voice inputs are becoming more important as smart assistants and mobile voice control become more prevalent.
Approach:	They propose to extend the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment.
Outcome:	The proposed corpus contains 49500 labeled speech segments covering 140 hours of audio.

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Lu Dai, Hao Liu, Hui Xiong

Information Retrieval

Challenge:	Existing methods for dense retrieval have demonstrated remarkable performance in IR tasks.
Approach:	They propose a method to improve the embedding of dense retrievers by using existence claim as a bridge.
Outcome:	The proposed method can be plugged into current dense retrieval methods and the results are published in the journal Nature.

Modeling Content Importance for Summarization with Pre-trained Language Models (2020.emnlp-main)

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Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin

A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)

Challenge:	Existing studies on content importance do not consider semantics and context when evaluating importance.
Approach:	They apply information theory to pre-trained language models to define the concept of importance from the perspective of information amount.
Outcome:	Experiments on CNN/Daily Mail and New York Times show that the proposed model can model the importance of content better than previous methods based on F1 and ROUGE scores.

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Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang

LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud (2024.findings-naacl)

Challenge:	Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs .
Approach:	They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding .
Outcome:	The proposed model shows an increase in performance in KIE and VQA tasks.

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Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Niloofar Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov

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

Challenge:	Currently, the server controls the generated text, but users can't keep it private . prompted generation is a common interaction paradigm for large language models on cloud .
Approach:	They propose a protocol where the server handles most of the computation while the client controls the sampling operation.
Outcome:	The proposed protocol protects both prompt and generation under strong attacks.

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

Question Answering

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

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.

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)

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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen

Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)

Challenge:	Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities.
Approach:	They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance.
Outcome:	The proposed method achieves a superior balance between downstream learning and general capability retention.

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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao

Information Retrieval

Challenge:	Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation.
Approach:	They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented .
Outcome:	The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines.

Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)

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Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao

Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)

Challenge:	Existing work on pretraining models for text classification uses image encoders instead of visual prompts.
Approach:	They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning.
Outcome:	The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets.

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Lu Xu, Hao Li, Wei Lu, Lidong Bing

Sentiment Analysis

Challenge:	Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Approach:	They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach.
Outcome:	The proposed model improves performance on multiple datasets and compares with existing models.

When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models (2026.acl-long)

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Xiaoxiao Ma, Kuofeng Gao, Zeyi Lu, Wenxi Jiang, Hao Fang, Hao Wu, Bin Chen, Shu-Tao Xia

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

Challenge:	Key-Value (KV) caching is widely used in large language models to enable long-context inference efficiently, yet its security implications remain underexplored.
Approach:	They propose a history-aware, per-head feedback merging strategy that prevents safety degradation while maintaining efficiency.
Outcome:	The proposed strategy prevents safety degradation while maintaining efficiency.

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

Dialogue Systems & Conversational Agents

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 .

DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)

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Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang

Text Generation

Challenge:	Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge .
Approach:	They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST .
Outcome:	The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance .

LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)

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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng

Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)

Challenge:	Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios.
Approach:	They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance.
Outcome:	The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures.

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Hao Zhou, Weidong Ren, Gongshen Liu, Bo Su, Wei Lu

Summarization

Challenge:	Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents.
Approach:	They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes.
Outcome:	The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance.

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach:	They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome:	The proposed model outperforms baselines on three real-world datasets.

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

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

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

CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering (2026.findings-acl)

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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Xiyin Zeng, Yi Lu, Hao Wang

LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)

Challenge:	Recent work has begun to address routing instability in VQA models by grouping similar concepts or routing based on examples.
Approach:	They propose a Concept-Guided Routing framework which incorporates semantics of the answer options to guide expert selection in the training phase.
Outcome:	The proposed framework delivers strong performance across multiple VQA tasks, demonstrating the effectiveness of the proposed framework.

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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)

Challenge:	Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms.
Approach:	They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content.
Outcome:	The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs.

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Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)

Challenge:	Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs.
Approach:	They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them.
Outcome:	The proposed method can detect safety-degrading features in benign data and remove them from the model.

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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen

Challenge:	Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data.
Approach:	They propose a location-based approach that leverages locational data to optimize interaction preferences.
Outcome:	The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations.

Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation (2025.naacl-long)

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Ruichu Cai, Junhao Lu, Zhongjie Chen, Boyan Xu, Zhifeng Hao

Low-Resource NLP

Challenge:	Zero-shot Named Entity Recognition (ZS-NER) aims to recognize entities in unseen domains without specific annotated data.
Approach:	They propose a novel two-stage framework leveraging large language model techniques to improve the ZS-NER’s recall rate.
Outcome:	The proposed framework improves the ZS-NER’s recall rate and accuracy by incorporating a large language model.

Neural Chinese Address Parsing (N19-1)

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Hao Li, Wei Lu, Pengjun Xie, Linlin Li

Challenge:	Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems.
Approach:	They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure.
Outcome:	The proposed model is able to capture complex dependencies between labels that cannot be readily captured by a simple linear-chain structure.

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)

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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu

Green & Sustainable NLP

Challenge:	Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored.
Approach:	They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain.
Outcome:	MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows.

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

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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

Learning Explicit and Implicit Structures for Targeted Sentiment Analysis (D19-1)

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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Hao Li, Wei Lu

Sentiment Analysis

Challenge:	Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space.
Approach:	They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model.
Outcome:	The proposed model outperforms existing models by capturing implicit and explicit structural information.

Learning with Structured Representations for Negation Scope Extraction (P18-2)

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Hao Li, Wei Lu

Representation Learning

Challenge:	Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information.
Approach:	They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information.
Outcome:	The proposed approaches can capture useful information such as features related to negation cue, long-distance dependencies and some latent structural information.

Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)

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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun

Text Generation

Challenge:	Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities.
Approach:	They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning.
Outcome:	The proposed approach outperforms existing LLMs on an open-source and industrial dataset.

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)

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Yongheng Zhang, Xu Liu, Ruoxi Zhou, Qiguang Chen, Hao Fei, Wenpeng Lu, Libo Qin

Reasoning

Challenge:	Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal.
Approach:	They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities .
Outcome:	The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs.

MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing (2026.findings-acl)

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Hongtao Duan, Lu Jiang, Minying Zhang, Xiaobing Zhu, Tianpeng Bu, Hao Jiang, Xinyu Wei, Lulu hu

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

Challenge:	Existing tool-calling methods rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved.
Approach:	They propose a method that decodes tool evidence from the tool library and mixes it into the output at the uncertain layer.
Outcome:	The proposed method reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead.

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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

PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)

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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Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024.eacl-long)

Challenge:	Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence.
Approach:	They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides.
Outcome:	The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides .

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Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yong Liu

Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)

Challenge:	Existing methods to recommend items are categorized into attribute-based and generation-based methods.
Approach:	They propose to represent items in natural language and formulate a conversational recommender system that can be optimized in a single stage without relying on non-textual metadata.
Outcome:	The proposed model can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph.

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Jiwei Zhang, Jianxun Lian, Haiming Qin, Mingyang Zhou, KeZhong Lu, Rui Mao, Hao Liao

Challenge:	Large Language Models (LLMs) have limited fault localization capabilities due to limited context length.
Approach:	They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs.
Outcome:	The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset.

SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings (2025.acl-long)

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Weikai Lu, Hao Peng, Huiping Zhuang, Cen Chen, Ziqian Zeng

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)

Challenge:	Existing low-resource security alignment methods struggle with the security risks posed by additional modalities.
Approach:	They propose to use multimodal datasets to enhance safety alignment but it is costly to construct these datasets.
Outcome:	Experiments on image, video, and audio-based MLLMs show that the proposed method can synthesize a high-quality embedding on a single RTX3090 GPU within 24 seconds.

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Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, Hao Liao

Explainability & Interpretability in NLP

Challenge:	Recent years have witnessed a growing interest in the development of explainable recommendation models.
Approach:	They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models.

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

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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.

Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

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Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie

R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)

Challenge:	Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach:	They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome:	The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.

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Haiming Qin, Jiwei Zhang, Wei Zhang, KeZhong Lu, Mingyang Zhou, Hao Liao, Rui Mao

Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)

Challenge:	Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning .
Approach:	They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities .
Outcome:	The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions.

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Zhongkai Sun, Yingxue Zhou, Jie Hao, Xing Fan, Yanbin Lu, Chengyuan Ma, Wei Shen, Chenlei Guo

Natural Language Interfaces

Challenge:	Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent.
Approach:	They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites.
Outcome:	The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites.

AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026.acl-long)

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Qingqing Lyu, Linjuan Wu, Yongliang Shen, Hengwei Liu, Hao Li, Shengpei Jiang, Yin Zhang, Weiming Lu

Challenge:	Existing automated approaches operate within fixed task schemas and often fail to autonomously discover new evaluation dimensions.
Approach:	They propose an automated framework that constructs domain-specific benchmarks directly from unstructured corpora using Bloom’s Taxonomy.
Outcome:	The proposed framework uncovers a broader and more fine-grained task space than expert-curated benchmarks while producing high-quality instances that preserve established model-level evaluation trends.

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

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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