Papers by Yanghua Xiao

WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)

Challenge:	Simile interpretation is a crucial task in natural language processing.
Approach:	They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions.
Outcome:	The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans.

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Xuwu Wang, Junfeng Tian, Min Gui, Zhixu Li, Rui Wang, Ming Yan, Lihan Chen, Yanghua Xiao

Knowledge Representation

Challenge:	Multimodal Entity Linking (MEL) is an essential task for many multimodal applications.
Approach:	They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models.
Outcome:	The proposed model uses the visual information of images more effectively than existing models.

SELFGOAL: Your Language Agents Already Know How to Achieve High-level Goals (2025.naacl-long)

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Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang

Challenge:	Existing approaches to improve the performance of language agents without training are not available.
Approach:	They propose an automatic approach to break down high-level goals into tree structure of more practical subgoals during interaction with environments while identifying the most useful subgoal.
Outcome:	The proposed approach significantly improves the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments.

The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)

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Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao

Reason from Fallacy: Enhancing Large Language Models’ Logical Reasoning through Logical Fallacy Understanding (2024.findings-naacl)

Challenge:	Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos.
Approach:	They propose a benchmark to evaluate and improve the cultural taboo safety of large language models.
Outcome:	The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos.

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Yanda Li, Dixuan Wang, Jiaqing Liang, Guochao Jiang, Qianyu He, Yanghua Xiao, Deqing Yang

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but struggle with some more complex reasoning tasks including logical reasoning.
Approach:	They propose five concrete tasks from three cognitive dimensions of WHAT, WHY, and HOW to evaluate LLMs’ capability of logical fallacy understanding.
Outcome:	The proposed dataset can be used to evaluate LLMs’ LFU capability and to fine-tune LLM models to obtain significantly enhanced performance on logical reasoning.

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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li

Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (D19-1)

Challenge:	Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly.
Approach:	They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show .
Outcome:	The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models.

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Xiaofei Shi, Yanghua Xiao

SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)

Challenge:	Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object.
Approach:	They propose to use dot product-based functions to define dot products over embeddings to better capture semantics of 1-N, N-1 and N-N relations.
Outcome:	The proposed framework outperforms existing methods on multilingual datasets.

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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao

Order Doesn’t Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2025.emnlp-main)

Challenge:	Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach:	They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome:	The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.

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Qianxi He, Qianyu He, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao

Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning (2026.acl-long)

Challenge:	Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inferences.
Approach:	They propose an order-centric data augmentation framework based on commutativity in logical reasoning that randomly shuffles independent premises to introduce condition order augmentation.
Outcome:	The proposed framework improves LLMs’ reasoning performance and adaptability to diverse logical structures.

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Sihang Jiang, Zhiyu Lu, Keyi Wang, Jiaqing Liang, Yanghua Xiao, Xiaojun Meng, Jiansheng Wei

HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)

Challenge:	Recent work on LLMs has focused on fine-grained skill decomposition and consistency probing at the propositional level.
Approach:	They propose a benchmark evaluating immediate inference that evaluates elemental operations over categorical propositions and proposes a model that uses immediate inferential reasoning.
Outcome:	The proposed benchmark demonstrates that models lack robust operator grounding, oscillating between structural reasoning and surface pattern matching, inconsistent handling of quantifiers and negation.

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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao

HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)

Challenge:	Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention.
Approach:	They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents.
Outcome:	The proposed model achieves a high 96% F1 score on data quality and is far lower than humans.

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Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, Yunwen Chen

Text Generation

Challenge:	Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation.
Approach:	They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion .
Outcome:	The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics.

GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)

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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao

MCiteBench: A Multimodal Benchmark for Generating Text with Citations (2025.findings-emnlp)

Challenge:	Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle.
Approach:	They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints.
Outcome:	Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints.

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Caiyu Hu, Yikai Zhang, Tinghui Zhu, Yiwei Ye, Yanghua Xiao

DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)

Challenge:	Existing work focuses on generating citations for text-only content . experimental results reveal MLLMs struggle to ground outputs reliably when handling multimodal input .
Approach:	They propose a benchmark to assess the ability of MLLMs to generate text with citations in multimodal contexts.
Outcome:	The proposed benchmark assesses the ability of MLLMs to generate text with citations in multimodal contexts.

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Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao

Challenge:	Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios.
Approach:	They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality.
Outcome:	The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%.

Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)

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Sirui Xia, Aili Chen, Xintao Wang, Tinghui Zhu, Yikai Zhang, Jiangjie Chen, Yanghua Xiao

Challenge:	Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning.
Approach:	They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions.
Outcome:	The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions.

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

CR-LLM: A Dataset and Optimization for Concept Reasoning of Large Language Models (2024.findings-acl)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Nianqi Li, Jingping Liu, Sihang Jiang, Haiyun Jiang, Yanghua Xiao, Jiaqing Liang, Zujie Liang, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han

AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)

Challenge:	Existing concept reasoning related datasets suffer from modeledge leakage and context leakage.
Approach:	They propose a concept reasoning for large language models with modeledge leakage prevention and context leakage preventive methods to improve the models' conceptual reasoning abilities.
Outcome:	The proposed method significantly improves the existing models and reasoning methods, achieving a 7% increase in accuracy compared to CoT and showing better granularity.

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Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen

Challenge:	Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website.
Approach:	They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks.
Outcome:	The proposed framework can handle diverse web environments more efficiently.

InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)

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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao

Revealing the Barriers of Language Agents in Planning (2025.naacl-long)

Challenge:	Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups.
Approach:	They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts.
Outcome:	The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities.

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Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao

Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)

Challenge:	Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood .
Approach:	They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions .
Outcome:	The proposed model achieves 15.6% on a real-world planning benchmark.

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Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao

EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models (2024.findings-acl)

Challenge:	Historical analogies are important abilities that help people make decisions and understand the world.
Approach:	They propose a historical analogy acquisition task that uses large language models to acquire historical analogies.
Outcome:	The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies.

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Yuyan Chen, Songzhou Yan, Sijia Liu, Yueze Li, Yanghua Xiao

Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency (2024.lrec-main)

Challenge:	Existing evaluations of emotional intelligence in large language models (LLMs) focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence.
Approach:	They propose a framework for evaluating the emotional intelligence of large language models (LLMs) that includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.
Outcome:	The proposed framework includes four distinct tasks: Key Event Recognition, Mixed Event Recognition and Implicit Emotional Recognition.

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Yuchen Shi, Deqing Yang, Jingping Liu, Yanghua Xiao, Zongyu Wang, Huimin Xu

Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)

Challenge:	Negation understanding is crucial to many downstream tasks such as sentiment analysis, question answering, Web search and natural language inference.
Approach:	They propose a novel negation triplet extraction task which aims to extract negation subject along with negation cue and scope.
Outcome:	The proposed model is based on a generative pretrained language model with a multi-task learning framework and achieves the best performance compared to baselines.

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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu

Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model.
Approach:	They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures.
Outcome:	The proposed framework yields significant performance gains on Twitter and other platforms.

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Bo Xu, Shufei Li, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

Programming Languages in NLP

Challenge:	Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries .
Approach:	They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors.
Outcome:	The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.

Improving Continual Relation Extraction through Prototypical Contrastive Learning (2022.coling-1)

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Chengwei Hu, Deqing Yang, Haoliang Jin, Zhen Chen, Yanghua Xiao

Human-in-the-loop Robotic Grasping Using BERT Scene Representation (2022.coling-1)

Challenge:	Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks.
Approach:	They propose a Continual Relation Extraction framework with Contrastive Learning which is built with a classification network and a prototypical contrastive network to achieve incremental-class learning of CRE.
Outcome:	The proposed framework outperforms the state-of-the-art methods on two public datasets and proves its effectiveness on improving performance.

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Yaoxian Song, Penglei Sun, Pengfei Fang, Linyi Yang, Yanghua Xiao, Yue Zhang

Data-Efficient Selection via Grammatical Complexity in Continual Pre-training of Domain-Specific LLMs (2025.emnlp-main)

Challenge:	Existing approaches for robotic grasping in cluttered scenes are expensive and lack structure information.
Approach:	They propose a human-in-the-loop framework for robotic grasping in cluttered scenes . they substitute scene-graph representation with a text representation of the scene using BERT .
Outcome:	The proposed framework outperforms object-agnostic and scene-graph based methods on robots and physical robots.

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Yizhou Ying, Geng Zhang, Cui Danxin, Chengyu Du, Guanglei Yue, Sihang Jiang, Jiaqing Liang, Yifei Fu, Hailin Hu, Yanghua Xiao

Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)

Challenge:	Existing data selection strategies for continual pre-training of large language models often rely on scarce labeled data or computationally expensive LLMs.
Approach:	They propose an annotation-independent data selection framework for CPT that evaluates grammatical complexity using lexical diversity and syntactic complexity.
Outcome:	The proposed framework outperforms baselines on a financial dataset and surpasses full-data training by 1.7% using only 20% of the data.

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Yuchen Ji, Bo Xu, Jie Shi, Jiaqing Liang, Deqing Yang, Yu Mao, Hai Chen, Yanghua Xiao

Semantic Parsing

Challenge:	Existing studies focus on a single query language, resulting in limited generalizability . a new task paradigm is proposed to unify semantic parsing tasks across different query languages .
Approach:	They propose a task paradigm that unifies parsing tasks across query languages . they identify query skeletons as a shared optimization target of Text-to-Query tasks .
Outcome:	The proposed method achieves state-of-the-art performance using only a small amount of synthesized data.

CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory (2025.findings-acl)

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Haokun Zhao, Jinyi Han, Jiaqing Liang, Yanghua Xiao, Xiaojun Meng, Jiansheng Wei

Linguistics & Cognitive NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but the complexity of emerging tasks and higher performance demands highlight the need for continuous improvement.
Approach:	They propose a method that refines evaluation results and characterizes model profiles at the knowledge component level.
Outcome:	The proposed method improves performance across multiple benchmarks and academic exams.

Why Did Apple Fall: Evaluating Curiosity in Large Language Models (2026.findings-acl)

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Haoyu Wang, Sihang Jiang, Yuyan Chen, Yitong Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao

Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries (2025.findings-acl)

Challenge:	Existing methods for evaluating curiosity-like behaviors in large language models lack curiosity-inspired features.
Approach:	They propose a psychology-inspired framework to evaluate curiosity in large language models . they adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs .
Outcome:	The proposed framework evaluates curiosity in large language models using questionnaires and behavioral studies.

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Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang

Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)

Challenge:	Current dense retrieval methods compute similarities between dense vectors but overlook the real query intents.
Approach:	They propose a neuro-symbolic information retrieval method that leverages first-order logic to optimize the embeddings of naive natural language by considering the logical consistency between queries and documents.
Outcome:	The proposed method outperforms existing methods on negative-constraint queries under zero-shot and low-resource retrieval tasks.

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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao

OVEL: Online Video Entity Linking (2025.coling-main)

Challenge:	Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training.
Approach:	They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity.
Outcome:	The proposed framework outperforms baseline models while maintaining high Affinity.

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Haiquan Zhao, Xuwu Wang, Shisong Chen, Zhixu Li, Xin Zheng, Yanghua Xiao

DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence? (2024.findings-emnlp)

Challenge:	Existing studies on Multi-modal Entity Linking focus on linking textual and visual mentions or offline videos’ mentions to entities in multi-modal knowledge bases.
Approach:	They propose a task called Online Video Entity Linking to establish connections between online videos and a knowledge base with high accuracy and timeliness.
Outcome:	The proposed method can establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness.

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Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu, Jiangjie Chen, Wenhao Huang, Yikai Zhang, Shusen Wang, Zheyu Ye, Yan Gao, Hongwei Feng, Yanghua Xiao

Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024.findings-emnlp)

Challenge:	Existing LLMs' abilities to detect evidence in long contexts are far inferior to humans.
Approach:	They propose a benchmark to assess LLMs' abilities in evidence and multi-step commonsense reasoning within a long context.
Outcome:	The proposed method improves the performance of LLMs in evidence detection and commonsense reasoning.

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Wenhao Huang, Qianyu He, Zhixu Li, Jiaqing Liang, Yanghua Xiao

Revisiting the Negative Data of Distantly Supervised Relation Extraction (2021.acl-long)

Challenge:	Definition bias is a negative phenomenon that can mislead models.
Approach:	They propose a framework that measures definition bias, bias-aware fine-tuning and task-specific bias mitigation to mitigate definition bias in information extraction.
Outcome:	The proposed framework mitigates definition bias in information extraction tasks by measuring definition bias, bias-aware fine-tuning, and task-specific bias mitigation.

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Chenhao Xie, Jiaqing Liang, Jingping Liu, Chengsong Huang, Wenhao Huang, Yanghua Xiao

ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)

Challenge:	Existing methods for relation extraction with distant supervision generate plenty of training samples but noisy labels and imbalanced training data cause problems.
Approach:	They propose a method that automatically labels a sentence with relational triples from a knowledge base.
Outcome:	The proposed method outperforms existing methods even with false positive samples.

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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao

Green & Sustainable NLP

Challenge:	Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability.
Approach:	They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training.
Outcome:	The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks.

Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)

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Zepeng Ding, Wenhao Huang, Jiaqing Liang, Yanghua Xiao, Deqing Yang

ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)

Challenge:	Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences.
Approach:	They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks.
Outcome:	The proposed framework integrates large language models with small models for relational triple extraction tasks.

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Tingting Liu, Chengyu Wang, Xiangru Zhu, Lei Li, Minghui Qiu, Jun Huang, Ming Gao, Yanghua Xiao

From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)

Challenge:	Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images.
Approach:	They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models.
Outcome:	The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts.

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Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao

HOTVCOM: Generating Buzzworthy Comments for Videos (2024.findings-acl)

Challenge:	Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills.
Approach:	They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions.
Outcome:	The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance.

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Yuyan Chen, Songzhou Yan, Qingpei Guo, Jiyuan Jia, Zhixu Li, Yanghua Xiao

Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024.findings-emnlp)

Challenge:	Existing research focuses on generating descriptive comments in English . hot-comments are important for video marketing and branding, authors say .
Approach:	They propose a framework to generate hot-comments on a Chinese video dataset . they use a combination of visual, auditory, and textual data to generate them .
Outcome:	The proposed framework shows that it generates hot-comments on both the new and existing datasets.

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Yiting Ran, Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Dr.Academy: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models (2024.acl-long)

Challenge:	Existing role-playing models focus on character knowledge and tones, but lack personality-indicative data to capture characters' minds.
Approach:	They propose to enhance role-playing agents (RPAs) via personality-indicative data by asking psychological scales to capture broad aspects of personality traits in individuals.
Outcome:	The proposed model exhibits advanced role-playing capabilities for both general and personality-related evaluations.

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Yuyan Chen, Songzhou Yan, Panjun Liu, Yanghua Xiao

Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021.acl-long)

Challenge:	Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning.
Approach:	They propose to evaluate the questioning capability in education as a teacher of large language models by evaluating their generated educational questions.
Outcome:	The proposed model can generate educational content that aligns with human perspectives and is more apt as an interdisciplinary teacher.

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Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjie Yi, Yanghua Xiao

Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)

Challenge:	Existing methods to extract relationships from texts depend on memory size and replay these memorized samples in subsequent tasks.
Approach:	They propose to use a model to extract relations between entities from texts where the samples of different relations are delivered into the model continuously.
Outcome:	The proposed model outperforms the state-of-the-art models and avoids catastrophic forgetting.

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Weiyuan Li, Xintao Wang, Siyu Yuan, Rui Xu, Jiangjie Chen, Qingqing Dong, Yanghua Xiao, Deqing Yang

Challenge:	a recent study has focused on simple settings, but their reliability in complex tasks remains understudied.
Approach:	They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases .
Outcome:	The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios.

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Challenge:	Existing methods focus on knowledge and linguistic patterns of characters.
Approach:	They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome:	The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models (2024.findings-emnlp)

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Qianyu He, Jie Zeng, Qianxi He, Jiaqing Liang, Yanghua Xiao

GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization (2024.findings-acl)

Challenge:	Large language models (LLMs) follow instructions with elaborate requirements, yet it remains under-explored how to enhance their ability to follow complex instructions with multiple constraints.
Approach:	They propose a method to obtain and utilize effective training data to enhance LLMs' ability to follow complex instructions with multiple constraints.
Outcome:	The proposed framework improves models' ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings while maintaining general capabilities.

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Chenghua Huang, Shisong Chen, Zhixu Li, Jianfeng Qu, Yanghua Xiao, Jiaxin Liu, Zhigang Chen

Metaphor Reasoning is Meta-reasoning (2026.acl-long)

Challenge:	Existing approaches to address address standardization are lacking in the current field.
Approach:	They propose a framework that incorporates spatial knowledge into address texts and achieves efficient address standardization.
Outcome:	The proposed framework incorporates spatial knowledge into address texts and achieves efficient address standardization.

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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao

Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)

Challenge:	Existing work on metaphor reasoning's impact on reasoning abilities is limited.
Approach:	They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable.
Outcome:	The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles.

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Qingyu Ren, Jie Zeng, Qianyu He, Jiaqing Liang, Yanghua Xiao, Weikang Zhou, Zeye Sun, Fei Yu

ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base (2024.acl-long)

Challenge:	In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models.
Approach:	They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method.
Outcome:	The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity.

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Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)

Challenge:	ANALOGYKB is a million-scale analogy knowledge base based on existing knowledge graphs (KGs) based upon relational knowledge triples, we can discover new analogies using the corresponding relations between concepts.
Approach:	They propose a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) ANALOGYKB identifies analogies of the same relations and analogies from analogous relations .
Outcome:	The proposed model enables both smaller LMs and LLMs to gain better analogical reasoning capabilities.

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Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang

Challenge:	Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data.
Approach:	They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data .
Outcome:	The proposed model outperforms competitors on several datasets.

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Dialogue Systems & Conversational Agents

Challenge:	Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach:	They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome:	The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.

TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation (2024.acl-long)

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Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen

Green & Sustainable NLP

Challenge:	e.g., GPT-4 still lag behind humans in effective multitasking, a study finds . current textual simulations do not adequately address the notion of time .
Approach:	They propose a textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios.
Outcome:	The proposed model incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios.

HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)

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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)

Challenge:	Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences.
Approach:	They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning.
Outcome:	The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks.

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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao

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

Challenge:	Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs.
Approach:	They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks.
Outcome:	The proposed method can optimize prompts for an LLM in downstream tasks.

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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.

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following (2026.acl-long)

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Qingyu Ren, Qianyu He, Powei Chang, Jie Zeng, Zeye Sun, Fei Yu, Jiaqing Liang, Yanghua Xiao

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)

Challenge:	Existing reinforcement learning approaches suffer from dependency on external supervision and sparse reward signals from multi-constraint tasks.
Approach:	They propose a self-supervised reinforcement learning framework that eliminates dependency on external supervision by deriving reward signals directly from instructions and generating pseudo-labels for reward model training.
Outcome:	The proposed framework achieves strong improvements across 3 in-domain and 5 out-of-domain datasets while maintaining computational efficiency.

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Sirui Xia, Xintao Wang, Jiaqing Liang, Yifei Zhang, Weikang Zhou, Jiaji Deng, Fei Yu, Yanghua Xiao

Information Retrieval

Challenge:	Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability.
Approach:	They propose a method that provides sentence-level citations in LLM-generated responses.
Outcome:	The proposed method achieves 90% accuracy in long-form question-answering tasks.

Causality-aware Concept Extraction based on Knowledge-guided Prompting (2023.acl-long)

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Siyu Yuan, Deqing Yang, Jinxi Liu, Shuyu Tian, Jiaqing Liang, Yanghua Xiao, Rui Xie

StrucText-Eval: Evaluating Large Language Model’s Reasoning Ability in Structure-Rich Text (2025.acl-long)

Challenge:	Concepts in knowledge graphs (KGs) are far from complete in existing knowledge graph models.
Approach:	They propose to equip a PLM-based extractor with a knowledge-guided prompt to alleviate concept bias by removing spurious co-occurrence correlations from existing knowledge.
Outcome:	The proposed prompt can alleviate concept bias and improve the performance of existing models.

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Zhouhong Gu, Haoning Ye, Xingzhou Chen, Zeyang Zhou, Hongwei Feng, Yanghua Xiao

Do Large Language Models have Problem-Solving Capability under Incomplete Information Scenarios? (2024.findings-acl)

Challenge:	Structured data has been central to corporate data strategies for decades . however, with the advancement of large language models (LLMs), there has been a significant shift towards the effective utilization of unstructured data.
Approach:	They propose an automatic evaluation data generation method to assess LLMs’ reasoning capabilities on structure-rich text.
Outcome:	The proposed method supports 8 structured languages and 29 tasks, generating data with adjustable complexity through controllable nesting and structural width.

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Yuyan Chen, Yueze Li, Songzhou Yan, Sijia Liu, Jiaqing Liang, Yanghua Xiao

FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)

Challenge:	Existing games such as "Who is undercover" are subjective and difficult to evaluate .
Approach:	They propose a game called BrainKing that evaluates LLMs' problem-solving capability under incomplete information scenarios.
Outcome:	The proposed game requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers.

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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao

Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)

Challenge:	Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge .
Approach:	They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance.
Outcome:	The proposed model improves performance on e-commerce image-text retrieval task by a large margin.

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Bo Xu, Haiqi Jiang, Jie Wei, Hongyu Jing, Ming Du, Hui Song, Hongya Wang, Yanghua Xiao

MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration (2025.findings-emnlp)

Challenge:	Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context.
Approach:	They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image .
Outcome:	The proposed method can be integrated into existing models and demonstrate consistent performance improvements.

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Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang, Feng Wei, Yanghua Xiao

GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick (2024.acl-long)

Challenge:	Program-of-Thought is an important way for LLMs to solve mathematical problems.
Approach:	They propose a multilingual programme reasoning method that uses program instead of natural language in reasoning and proposes to integrate multilingual integration into the training and inference.
Outcome:	The proposed method improves individual language’s reasoning accuracy by 2.5% and improves performance by 8%.

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Jiayi Fu, Xuandong Zhao, Ruihan Yang, Yuansen Zhang, Jiangjie Chen, Yanghua Xiao

What Makes an Ideal Quote? Recommending “Unexpected yet Rational” Quotations via Novelty (2026.acl-long)

Challenge:	Large language models generate human-like content, but they also pose a problem with generation diversity, negatively impacting generation diversity and user experience.
Approach:	They propose a Logits-Addition watermark and three variants that aim to enhance diversity to overcome generation diversity challenges.
Outcome:	The Logits-Addition watermark outperforms the Logits+Trick-based watermark in diversity tests and outperformed other decoding-based methods by 0.1 to 0.3.

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Powei Chang, Jin Xiao, Guanglei Yue, Qianyu He, Yanghua Xiao, Deqing Yang, Jiaqing Liang

Challenge:	Prior systems focus on topical relevance and overlook what makes quotes memorable.
Approach:	They propose a system that maps quotations and contexts into deep-meaning labels for label-enhanced retrieval.
Outcome:	The proposed system can recommend quotations that are contextually novel while semantically coherent.

Adaptive Ordered Information Extraction with Deep Reinforcement Learning (2023.findings-acl)

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Wenhao Huang, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Chuanjun Ji

Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models (2024.findings-acl)

Challenge:	Existing methods for information extraction follow a fixed extraction order for complex tasks with multiple elements to be extracted in one instance.
Approach:	They propose an adaptive ordered IE paradigm to find optimal element extraction order for different instances and a reinforcement learning framework to generate optimal order dynamically.
Outcome:	The proposed method beats existing methods and improves on several public datasets.

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Yikai Zhang, Qianyu He, Xintao Wang, Siyu Yuan, Jiaqing Liang, Yanghua Xiao

BOOKWORLD: From Novels to Interactive Agent Societies for Story Creation (2025.acl-long)

Challenge:	Multi-Modal Knowledge Graphs (MMKGs) are knowledge graphs that integrate and align information from diverse modalities (e.g., text and images).
Approach:	They propose a framework that integrates image-text pairs of long-tailed entities and a concept guidance module that offers explainability and enables human verification.
Outcome:	The proposed framework improves the accuracy of recognizing long-tailed image-text pairs compared to baselines and also offers flexibility and explainability.

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Yiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang, Yanghua Xiao, Deqing Yang

Challenge:	Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems.
Approach:	They propose a system for constructing and simulating book-based multi-agent societies that simulates established fictional worlds and characters.
Outcome:	The proposed system generates high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%.

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

Generative Entity Typing with Curriculum Learning (2022.emnlp-main)

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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Siyu Yuan, Deqing Yang, Jiaqing Liang, Zhixu Li, Jinxi Liu, Jingyue Huang, Yanghua Xiao

Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)

Challenge:	Entity typing fails to assign an entity to the types beyond the predefined type set.
Approach:	They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data .
Outcome:	The proposed model outperforms the state-of-the-art model on heterogeneous training data.

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Bo Xu, Shizhou Huang, Ming Du, Hongya Wang, Hui Song, Chaofeng Sha, Yanghua Xiao

Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following (2025.findings-acl)

Challenge:	Recent multimodal information extraction approaches overestimate the significance of images.
Approach:	They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities.
Outcome:	The proposed method outperforms existing models on two different multimodal information extraction tasks.

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Jie Zeng, Qianyu He, Qingyu Ren, Jiaqing Liang, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao

Challenge:	Existing large language models struggle to follow multi-constraint instructions in real-world applications.
Approach:	They propose to quantify the difficulty distribution of constraints by a novel Difficulty Distribution Index (CDDI) they find that LLMs are more performant when presented with constraints in a “hard-to-easy” order.
Outcome:	The proposed model is more performant when presented with constraints in a “hard-to-easy” order, compared with existing models with different architectures and sizes of parameters.

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning (2026.acl-long)

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Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, Hongcheng Guo

Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)

Challenge:	Group Relative Policy Optimization (GRPO) uses a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecting the varying contribution of individual reasoning steps.
Approach:	They introduce Outcome-grounded Advantage Reshaping (OAR) which redistributes advantages based on how much each token influences the model’s final answer.
Outcome:	Empirical results show that OAR-G outperforms GRPO on a high-fidelity attribution signal and suppresses low-impact tokens while preserving the advantage mass.

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Jie Shi, Xi Cao, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects.
Approach:	They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap.
Outcome:	The proposed framework outperforms existing methods that generate SQL queries directly.

CONSTRUCTURE: Benchmarking CONcept STRUCTUre REasoning for Multimodal Large Language Models (2024.findings-emnlp)

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Zhiwei Zha, Xiangru Zhu, Yuanyi Xu, Chenghua Huang, Jingping Liu, Zhixu Li, Xuwu Wang, Yanghua Xiao, Bei Yang, Xiaoxiao Xu

Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction (2023.findings-emnlp)

Challenge:	Existing multimodal large language models lack the ability to perceive the visual world with a deep concept structure cognition.
Approach:	They propose a concept-level benchmark to assess MLLMs’ hierarchical concept understanding and reasoning abilities.
Outcome:	The proposed model outperforms state-of-the-art models in concept structure reasoning evaluation.

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Siyu Yuan, Jiangjie Chen, Xuyang Ge, Yanghua Xiao, Deqing Yang

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)

Challenge:	Existing studies have focused on word analogies, but they neglect structures that underpin analogical reasoning.
Approach:	They propose a task to abduct structures that form an analogy between two systems to evaluate their analogical reasoning abilities.
Outcome:	The proposed task is based on 400 scientific analogies from 13 different fields and is compared with a standard SCAR benchmark.

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Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie, Jen-tse Huang, Jun Gao, Shuai Huang, Yueping Kang, Yuanli Guo, Hongwei Feng, Yanghua Xiao

Linguistics & Cognitive NLP

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs).
Approach:	They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.
Outcome:	The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters.

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)

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Jie Shi, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Chenhao Xie, Peng Wang, Wei Wang

Programming Languages in NLP

Challenge:	Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL.
Approach:	They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever .
Outcome:	The proposed method improves embedding-based retriever and reduces cost.

Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (P19-1)

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Jiangjie Chen, Ao Wang, Haiyun Jiang, Suo Feng, Chenguang Li, Yanghua Xiao

Challenge:	Existing generative methods overlook grammatical structure or make factual mistakes in generated texts.
Approach:	They propose a template-based method to ensure the readability of generated type descriptions . they also propose measurable metrics to measure the readibility of the generated type description .
Outcome:	The proposed method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.

Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)

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Ye Liu, Sheng Zhang, Rui Song, Suo Feng, Yanghua Xiao

Distilling Script Knowledge from Large Language Models for Constrained Language Planning (2023.acl-long)

Challenge:	Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing.
Approach:	They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities.
Outcome:	The proposed framework outperforms baselines by 16.5 - 27.8%.

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Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Jankowski, Yanghua Xiao, Deqing Yang

Character is Destiny: Can Persona-assigned Language Models Make Personal Choices? (2025.findings-emnlp)

Challenge:	Existing work exploits language models to plan for abstract goals of stereotypical activities, but leaves more specific goals with multi-facet constraints understudied.
Approach:	They propose an over-generate-then-filter approach to improve large language models on constrained language planning task by distilling a constrained script dataset.
Outcome:	The proposed approach improves the constrained language planning ability of large language models on constraint faithfulness and also in smaller LMs.

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Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, null Xiaoqingdong, Yanghua Xiao