Papers by Ying Xu

Anchoring-Guidance Fine-Tuning (AnGFT): Elevating Professional Response Quality in Role-Playing Conversational Agents (2025.emnlp-main)

Challenge:	Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors.
Approach:	They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively.
Outcome:	The proposed framework achieves state-of-the-art on the fine-grained content extraction task.

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Qibin Li, Zhen Xu, Shengyuan Bai, Nianmin Yao, Kaili Sun, Bowen Wu, Ying Li, Baoxun Wang

SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs).
Approach:	They propose an Anchoring-Guidance Fine-Tuning Framework to integrate relevant expert knowledge into RPCAs' training process to mitigate this issue.
Outcome:	The proposed framework significantly improves the RPCAs’ performance in handling role-specific professional queries while preserving their robust role-playing abilities.

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Zonghao Ying, Yangguang Shao, Jianle Gan, Gan Xu, Wenxin Zhang, Quanchen Zou, Junzheng Shi, Zhenfei Yin, Mingchuan Zhang, Aishan Liu, Xianglong Liu

Challenge:	Existing security benchmarks only cover user-level prompts and environmental threats . however, these models are vulnerable to pop-up attacks and prompt injections .
Approach:	They propose a security benchmark that covers a set of six attack vectors that span both user-level and environment-level manipulations.
Outcome:	The proposed security benchmarks cover a set of six real-world web environments with 2,970 adversarial trajectories and a multi-layered evaluation protocol dissecting agent failures across internal reasoning, behavioral execution, and task outcomes.

Xiaomingbot: A Multilingual Robot News Reporter (2020.acl-demos)

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Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yuping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei Li

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

Challenge:	Xiaomingbot is a multilingual and multimodal software robot with four capabilities: news generation, news translation, news reading and avatar animation.
Approach:	They propose to build a multilingual and multimodal software robot with four inte- gal capabilities: news generation, news translation, news reading and avatar animation.
Outcome:	The proposed system generates Chinese news, then reads it in multiple languages and generates an animated avatar reading it.

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu

Robustness in NLP

Challenge:	Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels.
Approach:	They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance .
Outcome:	The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously .

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)

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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer

Question Answering

Challenge:	Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements.
Approach:	They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills.
Outcome:	The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.

StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children’s Story-Based Learning (2024.emnlp-main)

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Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun

Natural Language Interfaces

Challenge:	Existing story reading systems fail to capture the nuances of how education experts think when conducting interactive story reading activities.
Approach:	They propose to use existing question-answering (QA) datasets to capture experts' annotations and thinking process to construct a story-based annotation framework.
Outcome:	The proposed framework captures experts’ annotations and thinking process and can be used to generate 5, 868 expert-annotated QA pairs with real-world knowledge.

Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation (2026.acl-industry)

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Minping Chen, Bing Xu, Zulong Chen, Chuanfei Xu, Ying Zhou, Zui Tao, Zeyi Wen

Neural Response Generation with Meta-words (P19-1)

Challenge:	Existing methods to measure the matching degree of a job and a candidate face several challenges, such as low-quality job descriptions and similar candidate-job pairs.
Approach:	They propose a large language model-based method that polishes and rewrites low-quality job descriptions by leveraging chain-of-thought prompts and category-aware Mixture of Experts (MoE) module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs.
Outcome:	The proposed method surpasses existing methods by 2.40% in AUC and 7.46% in GAUC and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses.

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Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang

Dialogue Systems & Conversational Agents

Challenge:	Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP .
Approach:	They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller.
Outcome:	The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy.

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

Semantic Text Processing

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)

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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao

TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)

Challenge:	Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks.
Approach:	They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison.
Outcome:	The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research.

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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng

Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)

Challenge:	Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users.
Approach:	They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification.
Outcome:	The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots.

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Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)

Challenge:	Named entity recognition (NER) is one of the fundamental tasks in natural language processing.
Approach:	They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner .
Outcome:	The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages.

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Liuyin Wang, Zihan Xu, Zibo Lin, Haitao Zheng, Ying Shen

Semantic Text Processing

Challenge:	Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information.
Approach:	They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation.
Outcome:	The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset.

Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER (2023.findings-emnlp)

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Jingyi Zhang, Ying Zhang, Yufeng Chen, Jinan Xu

Low-Resource NLP

Challenge:	Named entity recognition (NER) tasks require large datasets with accurate annotations that are labor-intensive and time-consuming.
Approach:	They propose a method to leverage domain gaps to model cross-domain few-shot named entity recognition (NER) NER is a natural language processing task to detect entity mentions and classify them into predefined labels .
Outcome:	The proposed method achieves state-of-the-art or competitive results on standard datasets.

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning (2026.acl-long)

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Zhirong Chen, Kaiyan Chang, Zhuolin Li, Cangyuan Li, Xinyang He, Chujie Chen, Mengdi Wang, Haobo Xu, Yinhe Han, Huawei Li, Ying Wang

Green & Sustainable NLP

Challenge:	Existing approaches to optimize Register-Transfer Level (RTL) code fail to simultaneously optimize functional correctness and hardware efficiency metrics such as Power, Performance, and Area (PPA).
Approach:	They propose a hierarchical reward based reinforcement learning framework that integrates direct feedback from EDA simulators and synthesis tools into a reward mechanism.
Outcome:	The proposed framework integrates direct feedback from EDA simulators and synthesis tools into a hierarchical reward based reinforcement learning framework.

Grey-box Adversarial Attack And Defence For Sentiment Classification (2021.naacl-main)

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Ying Xu, Xu Zhong, Antonio Jimeno Yepes, Jey Han Lau

Robustness in NLP

Challenge:	Recent advances in deep neural networks have created applications for a range of different domains.
Approach:	They propose a grey-box adversarial attack and defence framework for sentiment classification . they show that the framework produces an improved classifier that is robust in defending .
Outcome:	The proposed framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods.

The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models (2023.emnlp-main)

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Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang, Lifu Huang

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking (2021.acl-short)

Challenge:	Chain-of-Thought (CoT) prompting relies on the initial decisions, causing errors in early steps to accumulate and impact the final answers.
Approach:	They propose a divide-and-conquer style algorithm that leverages large language models to raise and answer sub-questions until collecting enough information to tackle the original one.
Outcome:	The proposed algorithm is more robust to errors and errors than CoT prompting and Tree-of-Thought prompting methods.

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Binzong Geng, Fajie Yuan, Qiancheng Xu, Ying Shen, Ruifeng Xu, Min Yang

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to learn consecutive tasks without forgetting how to perform previously trained problems are lacking.
Approach:	They propose a continual learning method which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks.
Outcome:	The proposed method preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks.

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)

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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

Text Generation

Challenge:	Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work.
Approach:	They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations.
Outcome:	The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work.

DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs (2026.acl-long)

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Wenzhuo Xu, Zhipeng Wei, Zonghao Ying, Deyue Zhang, Dongdong Yang, Xiangzheng Zhang, Quanchen Zou

X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)

Challenge:	Existing jailbreak methods only use a single image, restricting the attack space . Existing frameworks only use single image to distribute harmful requests across multiple images .
Approach:	They propose a compositional jailbreak framework that leverages Distributed instruction, Multimodal evidence and a Number chain task to fully enhance the jailbreak performance.
Outcome:	The proposed framework achieves attack success rates of over 90% on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 .

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Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu Huang

Text Generation

Challenge:	X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users.
Approach:	They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users.
Outcome:	The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality.

Multimodal Instruction Tuning with Conditional Mixture of LoRA (2024.acl-long)

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Ying Shen, Zhiyang Xu, Qifan Wang, Yu Cheng, Wenpeng Yin, Lifu Huang

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)

Challenge:	Multimodal Large Language Models (MLLMs) have demonstrated proficiency in diverse tasks across different domains.
Approach:	They propose a method that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA.
Outcome:	Experimental results show that MixLoRA outperforms LoRA with the same or higher ranks . MLLMs have demonstrated remarkable proficiency in diverse tasks across domains .

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Chun Zeng, Jiangjie Chen, Tianyi Zhuang, Rui Xu, Hao Yang, Qin Ying, Shimin Tao, Yanghua Xiao

Machine Translation

Challenge:	Existing approaches to lexically constrained neural machine translation suffer from high latency.
Approach:	They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints .
Outcome:	The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.

Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)

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Zihao Yi, Zhenqing Ling, Delong Zeng, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen

Semantic Text Processing

Challenge:	Large Language Models (LLMs) are sensitive to the contextual position of information in input.
Approach:	They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions.
Outcome:	Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.

Think and Recall: Layer-Level Prompting for Lifelong Model Editing (2025.emnlp-main)

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Jinke Wang, Zenan Ying, Qi Liu, Wei Chen, Tong Xu, Huijun Hou, Zhi Zheng

Low-Resource NLP

Challenge:	Existing methods for lifelong model editing suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits.
Approach:	They propose a plug-and-play method for knowledge retrieval and storage, i.e., Layer-Level Prompting, which enables seamless and efficient lifelong model editing.
Outcome:	The proposed method outperforms existing methods on question answering and hallucination benchmarks across different LLMs.

MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning (2023.acl-long)

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Zhiyang Xu, Ying Shen, Lifu Huang

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models (2025.emnlp-main)

Challenge:	Experimental results show zero-shot performance on unseen multimodal tasks . instruction tuning has yet to be explored for vision and multimodal task.
Approach:	They propose a multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories.
Outcome:	The proposed model performs well on unseen multimodal tasks and is highly scalable.

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Shrey Pandit, Jiawei Xu, Junyuan Hong, Zhangyang Wang, Tianlong Chen, Kaidi Xu, Ying Ding

Reasoning

Challenge:	Recent advances in Large Language Models (LLMs) generate plausible but factually incorrect outputs, posing serious risks to patient safety and clinical decision-making.
Approach:	They propose a benchmark for medical hallucination detection using 10,000 question-answer pairs derived from PubMedQA.
Outcome:	The proposed model achieves an F1 score as low as 0.625 for detecting 'hard' category hallucinations.

LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)

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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying

Challenge:	Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice.
Approach:	They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues.
Outcome:	The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability.

R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation (2025.emnlp-main)

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Kaijie Chen, Zihao Lin, Zhiyang Xu, Ying Shen, Yuguang Yao, Joy Rimchala, Jiaxin Zhang, Lifu Huang

RanLoRA: Residual-aware Nonlinear Low-Rank Adaptation (2026.findings-acl)

Challenge:	Reasoning is a fundamental capability underpinning text-to-image (T2I) generation.
Approach:	They propose a benchmark to rigorously assess reasoning-driven T2I generation.
Outcome:	Experiments with 16 representative T2I models show limited reasoning performance . a strong pipeline-based framework decouples reasoning and generation .

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Xu Luo, Yongbin Liu, Chunping Ouyang, Ying Yu

Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)

Challenge:	Low-Rank Adaptation (LoRA) relying on linear low-rank projections restricts adaptation to linear subspaces, limiting flexibility on complex downstream tasks.
Approach:	They propose a nonlinear low-rank Adaptation approach that leverages pretrained weights to decompose them into principal components that are kept frozen and residual components that can be used for task-specific adaptation.
Outcome:	The proposed approach outperforms vanilla LoRA and representative variants on commonsense reasoning, image classification, and mathematical reasoning tasks.

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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang

Entropy-Aware Reshaping of Reinforcement Signals for Multi-Answer Reasoning (2026.findings-acl)

Challenge:	Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data.
Approach:	They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data.
Outcome:	The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks.

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Zhi Li, Huidan Xu, Zhen Hu, Yali Du, Ying Liu

Reasoning

Challenge:	Reinforcement learning with verifiable rewards (RLVR) is a standard post-training paradigm for large language models.
Approach:	They propose a framework that reshapes how learning signals are normalized and aggregated.
Outcome:	Experiments on MCTACO and MMLU-Multi show that the proposed framework improves accuracy, training stability and cross-dataset transfer performance.

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

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Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

Information Extraction & Text Mining

Challenge:	Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach:	They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome:	The proposed model improves on two representative datasets and improves performance.

APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)

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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing (2025.naacl-long)

Challenge:	APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need.
Approach:	They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities .
Outcome:	The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy.

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Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have demonstrated their effectiveness in human-guided dialogues, but tasks in the real world are more complex and require greater autonomy from LLMs.
Approach:	They propose to characterize LLM-guided conversation into three fundamental components: Goal Navigation, Context Management, Empathetic Engagement and implement an interviewing environment for the evaluation of LLMs.
Outcome:	The proposed LLM outperforms baseline LLMs in interviewing quality and autobiography generation quality.

PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning (2025.acl-long)

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Zhicong Lu, Changyuan Tian, PeiguangLi PeiguangLi, Li Jin, Sirui Wang, Wei Jia, Ying Shen, Guangluan Xu

Asymmetric Relational-Geometry Driven Universal Adversarial Perturbations for Vision-Language Models (2026.findings-acl)

Challenge:	Existing studies on Large Language Models (LLMs) have failed to evaluate their performance in event reasoning with a single event relational type or reasoning format.
Approach:	They propose a benchmark to evaluate LLMs' event reasoning capability using a single event relational type or reasoning format.
Outcome:	The proposed model improves on 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity.

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Jiaxin Ye, Weihai Li, Ying Wang, Simeng Qin, Zhitao Zeng, Zikai Xu

Robustness in NLP

Challenge:	Existing universal adversarial perturbation (UAP) methods suffer from limited cross-model transferability in black-box scenarios.
Approach:	They propose an optimization-based framework that learns universal perturbations under an asymmetric relational-geometry driven objective.
Outcome:	The proposed framework outperforms state-of-the-art models in black-box transfer settings.

Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)

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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia