Papers by Weiming Lu

Advancing Process Verification for Large Language Models via Tree-Based Preference Learning (2024.emnlp-main)

Challenge:	Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems.
Approach:	They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features.
Outcome:	The proposed model achieves state-of-the-art on four widely used benchmarks.

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Mingqian He, Yongliang Shen, Wenqi Zhang, Zeqi Tan, Weiming Lu

Language Models

Challenge:	Existing methods for generating step-by-step rationales fail to fully utilize the relative merits of intermediate steps, limiting the effectiveness of feedback provided.
Approach:	They propose a tree-based preference learning verifier that constructs reasoning trees via a best-first search algorithm and collects step-level paired data for preference training.
Outcome:	The proposed approach outperforms existing benchmarks on arithmetic and commonsense reasoning tasks.

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

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

Robustness in NLP

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

Insert or Attach: Taxonomy Completion via Box Embedding (2024.acl-long)

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Wei Xue, Yongliang Shen, Wenqi Ren, Jietian Guo, Shiliang Pu, Weiming Lu

Representation Learning

Challenge:	Existing taxonomy expansion methods embed concepts as vectors in Euclidean space, causing incorrectly model asymmetric relations.
Approach:	They propose to use box containment and center closeness to create geometric scorers that capture intrinsic relationships between concepts.
Outcome:	The proposed framework outperforms existing methods on four real-world datasets.

Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)

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Hengwei Liu, Yongliang Shen, Zhe Zheng, Haoyuan Ma, Xingyu Wu, Yin Zhang, Weiming Lu

Information Retrieval

Challenge:	Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information .
Approach:	They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement.
Outcome:	The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base.

Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)

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Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang

Responsible & Trustworthy NLP

Challenge:	Existing models lack interpretability due to the neglect of rationale in the prediction process.
Approach:	They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability.
Outcome:	The proposed framework provides good interactivity and interpretability which enables practical use.

Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization (2024.acl-long)

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Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu

AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) are designed as specific task solvers with sophisticated prompt engineering, but are inherently incapacitating to address complex dynamic scenarios.
Approach:	They propose an LLM-based agent with policy-level reflection and optimization that can learn from interactive experiences and progressively elevate its behavioral policy.
Outcome:	The proposed agent outperforms vanilla LLM and specialized models in blackjack and Texas hold’em.

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Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu

Responsible & Trustworthy NLP

Challenge:	Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification.
Approach:	They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth.
Outcome:	The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation.

A Set Prediction Network For Extractive Summarization (2023.findings-acl)

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Xiaoxia Cheng, Yongliang Shen, Weiming Lu

CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution (2026.acl-long)

Challenge:	Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences.
Approach:	They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel .
Outcome:	The proposed method outperforms previous state-of-the-art models on extracted summary datasets.

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Teng Pan, Yuchen Yan, Zixuan Wang, Ruiqing Zhang, Guiyang Hou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)

Challenge:	Label-free reinforcement learning enables large language models to improve reasoning capabilities . but as training maximizes self-consistency, output diversity collapses, authors say . authors propose a framework where a single model alternates between generator and verifier roles .
Approach:	They propose a framework where a model alternates between generator and verifier roles, bootstrapping each other.
Outcome:	Experiments show that CoVerRL outperforms label-free baselines on reasoning benchmarks . the framework can be used to improve reasoning abilities without ground-truth supervision .

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Yifan Lu, Ziqi Zhang, Chunfeng Yuan, Jun Gao, Congxuan Zhang, Xiaojuan Qi, Bing Li, Weiming Hu

Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)

Challenge:	Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection.
Approach:	a new method is proposed to help model-generated hallucinations without external dependencies.
Outcome:	a new method that self-injects hallucinations into a generated response improves halluuutations mitigation.

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Yifei Liu, Yiquan Wu, Ang Li, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang

MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain.
Approach:	They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task.
Outcome:	The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022.

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Shuhui Wu, Yongliang Shen, Zeqi Tan, Wenqi Ren, Jietian Guo, Shiliang Pu, Weiming Lu

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts (2026.acl-long)

Challenge:	Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with knowledge bases or gazetteers and unlabeled corpus.
Approach:	They propose a noise-robust prototype network named MProto for a DS-NER task . they propose an optimal transport algorithm to mitigate the noise from incomplete labeling .
Outcome:	The proposed network achieves state-of-the-art on several DS-NER benchmarks.

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Haolei Xu, Haiwen Hong, Hongxing Li, Rui Zhou, Yang Zhang, Longtao Huang, Hui Xue, Yongliang Shen, Weiming Lu, Yueting Zhuang

Scaling LLMs’ Social Reasoning: Sprinkle Cognitive “Aha Moment” into Fundamental Long-thought Logical Capabilities (2025.findings-acl)

Challenge:	Existing multimodal Mixture-of-Experts models accurately perceive image content yet fail in subsequent reasoning . Seeing but not thinking phenomenon is a puzzling phenomenon .
Approach:	They propose a routing-guided intervention method that enhances domain expert activation.
Outcome:	The proposed method achieves consistent improvements on visual reasoning tasks.

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Guiyang Hou, Wenqi Zhang, Zhe Zheng, Yongliang Shen, Weiming Lu

Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem (2022.findings-emnlp)

Challenge:	Existing studies have examined how large language models’ social reasoning capabilities evolve during model size scaling or reasoning tokens scaling.
Approach:	They propose to optimize evaluation of Large Language Models from both data and model perspectives and to analyze their reasoning trajectories to identify notable cognitive "Aha Moments"
Outcome:	The proposed model outperforms the o1-preview model by 19.0 points in the evaluation of large language models.

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Wenqi Zhang, Yongliang Shen, Yanna Ma, Xiaoxia Cheng, Zeqi Tan, Qingpeng Nong, Weiming Lu

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)

Challenge:	Existing methods for generating complex semantics and diverse equations are limited by a fixed view.
Approach:	They propose a multi-view consistent contrastive learning approach that decouples human reasoning into two independent but consistent views.
Outcome:	The proposed approach significantly outperforms existing baselines on complex problems on multiple languages.

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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang

Green & Sustainable NLP

Challenge:	Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
Approach:	They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents.
Outcome:	The proposed framework leverages the strength of both LLM and domain models in the context of precedents.

De-Bias for Generative Extraction in Unified NER Task (2022.acl-long)

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Shuai Zhang, Yongliang Shen, Zeqi Tan, Yiquan Wu, Weiming Lu

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

Challenge:	Existing methods for Named entity recognition (NER) are not consistent with the task, which makes the model vulnerable to incorrect biases.
Approach:	They propose to use generative model to recognize entities from sentences . they analyze incorrect biases in the generation process from a causal perspective .
Outcome:	The proposed method improves the performance of the generative NER model in various datasets.

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Information Retrieval

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

An Expression Tree Decoding Strategy for Mathematical Equation Generation (2023.emnlp-main)

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Wenqi Zhang, Yongliang Shen, Qingpeng Nong, Zeqi Tan, Yanna Ma, Weiming Lu

Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)

Challenge:	Existing approaches to generate mathematical equations from natural language ignore parallel or dependent relations between math expressions.
Approach:	They propose to integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy.
Outcome:	The proposed method outperforms baseline methods for generating mathematical equations from natural language.

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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang

Green & Sustainable NLP

Challenge:	Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant.
Approach:	They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs.
Outcome:	The proposed method improves performance on a range of natural language processing tasks.

Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)

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Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang

STaR-SQL: Self-Taught Reasoner for Text-to-SQL (2025.acl-long)

Challenge:	Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections.
Approach:	They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step .
Outcome:	The proposed method achieves state-of-the-art on five widely used benchmarks.

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Mingqian He, Yongliang Shen, Wenqi Zhang, Qiuying Peng, Jun Wang, Weiming Lu

Programming Languages in NLP

Challenge:	Existing methods for generating step-by-step “chain-of-thought” rationales are limited to text-to-SQL.
Approach:	They propose a method that prompts SQL query generation to produce reasoning steps for SQL queries and fine-tunes it on rationales that lead to correct outcomes.
Outcome:	The proposed method outperforms agent-like prompting methods on the Spider benchmark.

Good Meta-tasks Make A Better Cross-lingual Meta-transfer Learning for Low-resource Languages (2023.findings-emnlp)

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Linjuan Wu, Zongyi Guo, Baoliang Cui, Haihong Tang, Weiming Lu

Cross-Lingual Transfer

Challenge:	Model-agnostic meta-learning has garnered attention as a promising technique for enhancing few-shot cross-lingual transfer learning in low-resource scenarios.
Approach:	They propose a Meta-Task Collector-based Cross-lingual Meta-Transfer framework to adapt data selection strategies to construct cross-lingual meta-tasks to reduce language gaps.
Outcome:	The proposed framework significantly improves model performance in the target language with minimal annotation costs.

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

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Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang

Low-Resource NLP

Challenge:	Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates.
Approach:	They propose to unify entity locating and entity typing into prompt learning by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities.
Outcome:	The proposed model outperforms the state-of-the-art model in a few-shot setting . it uses a template filled with multiple prompts and a bipartite graph matching mechanism .

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)

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Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Representation Learning

Challenge:	Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions.
Approach:	They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method.
Outcome:	The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets.

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)

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Yiquan Wu, Anlai Zhou, Yuhang Liu, Yifei Liu, Adam Jatowt, Weiming Lu, Jun Xiao, Kun Kuang

Low-Resource NLP

Challenge:	In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process.
Approach:	They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains.
Outcome:	The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research.

Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training (2025.emnlp-main)

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Linjuan Wu, Hao-Ran Wei, Huan Lin, Tianhao Li, Baosong Yang, Fei Huang, Weiming Lu

Multilinguality

Challenge:	Existing methods for enhancing cross-lingual transfer are limited by parallel resources and lack linguistic and domain coverage.
Approach:	They propose a cross-lingual in-context pre-training approach that leverages semantically related bilingual Wikipedia documents to enhance cross-linguistic transfer.
Outcome:	The proposed approach improves multilingual performance on three models across six target languages.

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

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

Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)

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

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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang

Knowledge Representation

Challenge:	Named entity recognition is a fundamental task in natural language processing.
Approach:	They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner.
Outcome:	The proposed method outperforms existing state-of-the-art models on nested and flat datasets.

Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)

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Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang

Natural Language Interfaces

Challenge:	Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored.
Approach:	They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks.
Outcome:	The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively.

Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)

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Yiquan Wu, Weiming Lu, Yating Zhang, Adam Jatowt, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus.
Approach:	They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses.
Outcome:	The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets.

Minimally-Supervised Relation Induction from Pre-trained Language Model (2022.findings-naacl)

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Lu Sun, Yongliang Shen, Weiming Lu

From English to Second Language Mastery: Enhancing LLMs with Cross-Lingual Continued Instruction Tuning (2025.acl-long)

Challenge:	Existing methods to induce relation in NLP depend heavily on word embeddings.
Approach:	They propose a method to induce relation with BERT under minimal supervision . they first extract proper templates from corpus and then use BERT attention weights to represent the pseudo-sentences.
Outcome:	The proposed method achieves state-of-the-art in relation induction tasks on Google Analogy Test Sets, Bigger Analogy test set (BATS) and DiffVec.

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Linjuan Wu, Hao-Ran Wei, Baosong Yang, Weiming Lu

Multilinguality

Challenge:	Large Language Models (LLMs) acquire strong language skills through extensive pre-training and supervised fine-tuning (SFT) on instructionresponse pairs.
Approach:	They propose a method which leverages translation-based parallel instruction data to enhance cross-lingual adaptability.
Outcome:	The proposed model improves on Llama-2-7B across five languages against three objective benchmarks and an LLM-as-a-judge benchmark.

Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024.acl-long)

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Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu

Struct-XLM: A Structure Discovery Multilingual Language Model for Enhancing Cross-lingual Transfer through Reinforcement Learning (2023.emnlp-main)

Challenge:	Recent research indicates without external feedback, LLM’s intrinsic reflection is unstable.
Approach:	They propose a method that combines self-evaluated and external feedback to improve LLM's reflection.
Outcome:	The proposed method improves the quality of self-evaluated feedback and can catalyze more accurate and stable reflection.

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Linjuan Wu, Weiming Lu

Multilinguality

Challenge:	Existing methods require syntactic labels that are difficult to obtain and of poor quality for low-resource languages.
Approach:	They propose a syntactic alignment model that leverages reinforcement learning to discover universal syntaktic structures for cross-lingual PLM alignment.
Outcome:	The proposed model improves cross-lingual representation alignment on the XTREME benchmark.

Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese (2026.acl-long)

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Xing Yue, Yongliang Shen, Weiming Lu

Syntactic Text Processing

Challenge:	Existing benchmarks on LLMs’ phonological abilities are either solvable through rote memorization or intertwined with other abilities, making them inadequate to measure LLM’s genuine ability in phonological understanding.
Approach:	They propose to use a Chinese benchmark to evaluate LLMs' phonological understanding to test their ability to recall correct pronunciations.
Outcome:	The proposed benchmarks show that LLMs excel at recalling correct pronunciations, but struggle to leverage phonological knowledge in the flexible and intuitive way that human speakers do.

Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)

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Yongliang Shen, Xinyin Ma, Zeqi Tan, Shuai Zhang, Wen Wang, Weiming Lu

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Named entity recognition (NER) is a well-studied task in natural language processing.
Approach:	They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them .
Outcome:	The proposed method outperforms state-of-the-art models on nested NER datasets.

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

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

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

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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.

Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment (2022.emnlp-main)

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Yechun Tang, Xiaoxia Cheng, Weiming Lu

Question Answering

Challenge:	ALCQA addresses the semantic and structural gap between natural language and action sequences . a priori, the semantics of the question and action are not well understood .
Approach:	They propose an alignment-enhanced complex question answering framework which aligns questions and actions into sequences.
Outcome:	The proposed framework outperforms state-of-the-art methods on a CQA and WQSP dataset.

TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models’ Theory-of-Mind (2024.findings-acl)

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Guiyang Hou, Wenqi Zhang, Yongliang Shen, Linjuan Wu, Weiming Lu

Language Models

Challenge:	Theory of Mind (ToM) is the foundation of social interaction and is crucial for social interaction.
Approach:	They propose a tool-belief solver that can transform a character’s higher-order beliefs into another character’ s first-order belief under belief communication period.
Outcome:	The proposed model improves the ToM capabilities of Large Language Models (LLMs) in multiple scenarios.

Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)

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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang