Papers by Juanzi Li

LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)

Challenge:	Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression.
Approach:	They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression.
Outcome:	The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline.

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Yi Jing, Zijun Yao, Hongzhu Guo, Lingxu Ran, Xiaozhi Wang, Lei Hou, Juanzi Li

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)

Challenge:	Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus.
Approach:	They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders.
Outcome:	The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs .

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Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, Maosong Sun

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)

Challenge:	Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training.
Approach:	They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning.
Outcome:	Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing.

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Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong

Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)

Challenge:	Existing methods for learning word and entity representations in monolingual settings are limited.
Approach:	They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences.
Outcome:	The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts.

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Mengying Lu, Yuquan Wang, Jifan Yu, Yexing Du, Lei Hou, Juanzi Li

Improving Event Detection via Open-domain Trigger Knowledge (2020.acl-main)

Challenge:	Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC.
Approach:	They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations.
Outcome:	The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score.

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Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie

LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)

Challenge:	Existing methods for event detecting are prone to overfitting densely labeled trigger words due to the small scale of training data.
Approach:	They propose a novel Enrichment Knowledge Distillation model to leverage external open-domain trigger knowledge to reduce in-built biases to frequent trigger words in annotations.
Outcome:	The proposed model outperforms nine strong baselines and is especially effective for unseen/sparsely labeled trigger words.

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Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li

Question Answering

Challenge:	Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations.
Approach:	They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC.
Outcome:	The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o.

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)

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Xin Lv, Yuxian Gu, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu

OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding (2023.emnlp-demo)

Challenge:	Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations .
Approach:	They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios.
Outcome:	The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL.

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Hao Peng, Xiaozhi Wang, Feng Yao, Zimu Wang, Chuzhao Zhu, Kaisheng Zeng, Lei Hou, Juanzi Li

WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)

Challenge:	Event understanding is fundamental for humans to understand the world.
Approach:	They propose an event understanding toolkit called OmniEvent that is comprehensive and fair . it supports mainstream modeling paradigms and the processing of 15 widely-used datasets .
Outcome:	The toolkit supports mainstream modeling paradigms and the processing of 15 widely-used English and Chinese datasets.

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Shangqing Tu, Yuliang Sun, Yushi Bai, Jifan Yu, Lei Hou, Juanzi Li

Trigger-Argument based Explanation for Event Detection (2023.findings-acl)

Challenge:	Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection.
Approach:	They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance.
Outcome:	The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality.

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Yong Guan, Jiaoyan Chen, Freddy Lecue, Jeff Pan, Juanzi Li, Ru Li

MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)

Challenge:	Existing works on ED use words or phrases to explain models’ inner mechanisms, but for ED, the event structure is more enlightening clues to explain model behaviors.
Approach:	They propose a Trigger-Argument based Explanation method which can utilize event structure knowledge to uncover a faithful interpretation for existing ED models at neuron level.
Outcome:	The proposed method can reveal the process by which the model predicts on the large-scale MAVEN and the widely-used ACE 2005 datasets.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou

MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)

Challenge:	Existing datasets exhibit data scarcity and limited coverage of general-domain events.
Approach:	They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types.
Outcome:	The proposed dataset shows that existing methods cannot achieve promising results on the small datasets.

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Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li

Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022.findings-emnlp)

Challenge:	Existing datasets for event understanding have limited coverage due to complexity of tasks.
Approach:	They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation .
Outcome:	The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.

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Xirui Ke, Jing Zhang, Xin Lv, Yiqi Xu, Shulin Cao, Cuiping Li, Hong Chen, Juanzi Li

Natural Language Interfaces

Challenge:	Recent rise of conversational applications has promoted the development of conversation KBQA (ConvKBQA).
Approach:	They propose a framework to produce a full-fledged rewritten question based on conversation history and then reason the answer by existing single-turn KBQA models.
Outcome:	The proposed framework produces a full-fledged rewritten question based on the conversation history and reasoned the answer by existing single-turn KBQA models.

Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)

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Yuchen Fan, Yantao Liu, Zijun Yao, Jifan Yu, Lei Hou, Juanzi Li

COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)

Challenge:	Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods.
Approach:	They propose a method to evaluate large language models by incorporating a human annotation schema.
Outcome:	The proposed evaluation method improves matching between model outputs and golden labels.

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Hao Peng, Xiaozhi Wang, Shengding Hu, Hailong Jin, Lei Hou, Juanzi Li, Zhiyuan Liu, Qun Liu

Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)

Challenge:	Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge.
Approach:	They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts.
Outcome:	The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts.

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Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, Jinghui Xiao

Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)

Challenge:	Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult.
Approach:	They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB.
Outcome:	The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP.

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Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng, Jifan Yu, Jinxin Liu, Lei Hou, Juanzi Li, Xu Bin

KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)

Challenge:	Existing evaluation benchmarks focus on pairwise matching, ignoring robustness . current models exhibit frustrating degradation, with a maximum drop of 23.43 F1 score .
Approach:	They propose a benchmark that simulates the evaluation of open information extraction models in the real world . they perform experiments on typical models published in the last decade and a representative large language model .
Outcome:	The proposed model is rated robust on a knowledge-invariant clique with different syntactic and expressive forms.

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Jie Zhou, Shengding Hu, Xin Lv, Cheng Yang, Zhiyuan Liu, Wei Xu, Jie Jiang, Juanzi Li, Maosong Sun

Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach (2023.findings-emnlp)

Challenge:	Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC).
Approach:	They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links .
Outcome:	The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty.

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Zheyuan Zhang, Jifan Yu, Juanzi Li, Lei Hou

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)

Challenge:	Existing studies on LLMs evaluation with exams are lacking in cognitive research on their overall knowledge structure.
Approach:	They conduct an evaluation using a human test dataset based on Bloom Taxonomy to reveal the knowledge structures of Large Language Models and gain insights of their cognitive capabilities.
Outcome:	The proposed model can pass AP, SAT, and Leetcode exams, but lacks the cognitive power to perform on human exams.

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Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, Jian Tang

AdaptThink: Reasoning Models Can Learn When to Think (2025.emnlp-main)

Challenge:	Existing language representation models (PLMs) cannot capture factual knowledge from text.
Approach:	They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM.
Outcome:	The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks.

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Jiajie Zhang, Nianyi Lin, Lei Hou, Ling Feng, Juanzi Li

WildReward: Learning Reward Models from In-the-Wild Human Interactions (2026.acl-long)

Challenge:	Recent advances in large reasoning models have demonstrated remarkable capabilities in tackling complex tasks.
Approach:	They propose an algorithm to teach reasoning models to choose the optimal thinking mode based on problem difficulty.
Outcome:	The proposed algorithm reduces the average response length and improves accuracy on three math datasets.

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Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao, Lei Hou, Juanzi Li

Challenge:	Prior work focused on collecting preference pairs, requiring substantial annotation efforts.
Approach:	They propose a pipeline to extract reliable human feedback from in-the-wild interactions . they propose to use WildChat as an interaction source to train the model .
Outcome:	The proposed model achieves comparable or even superior performance compared to conventional models with improved calibration and cross-sample consistency.

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making (2021.acl-long)

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Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Yichi Zhang, Zelin Dai

KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases (2024.emnlp-main)

Challenge:	Entity Matching (EM) aims at recognizing entity records that denote the same real-world object.
Approach:	They propose a novel EM framework that consists of Heterogeneous Information Fusion and Key Attribute Tree Induction to decouple feature representation from matching decision.
Outcome:	The proposed framework outperforms SOTA EM models on 6 public datasets and 3 industrial datasets.

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Jiajie Zhang, Shulin Cao, Linmei Hu, Ling Feng, Lei Hou, Juanzi Li

LongReward: Improving Long-context Large Language Models with AI Feedback (2025.acl-long)

Challenge:	Program induction (PI) is a promising paradigm for using knowledge bases (KBs) to help large language models answer complex knowledge-intensive questions.
Approach:	They propose a plug-and-play framework that enables large language models to induce programs over any low-resourced KB.
Outcome:	Experiments show that KB-Plugin outperforms SoTA low-resourced PI methods with 25x smaller backbone LLM on large-scale and domain-specific KBs and even approaches the performance of supervised methods.

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Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li

How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)

Challenge:	In recent years, significant advancements have been achieved in the development of long-context large language models (LLMs).
Approach:	They propose a method that utilizes an off-the-shelf LLM to provide rewards for long-context model responses from four human-valued dimensions: helpfulness, logicality, faithfulness, and completeness.
Outcome:	The proposed method improves models’ long-context performance and enhances their ability to follow short instructions.

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Hailong Jin, Tiansi Dong, Lei Hou, Juanzi Li, Hui Chen, Zelin Dai, Qu Yincen

Multilinguality

Challenge:	Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer.
Approach:	They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning.
Outcome:	The proposed model outperforms baselines and can handle negative transfer.

Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)

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Xiaozhi Wang, Shengyu Jia, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Jie Zhou

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)

Challenge:	Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles.
Approach:	They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments .
Outcome:	The proposed model can achieve comparable results to existing methods on two widely-used datasets.

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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

Reasoning

Challenge:	Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks.
Approach:	They propose a benchmark to assess the ability of long-context large language models to handle long-text problems.
Outcome:	The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint .

RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)

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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li

Challenge:	Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale.
Approach:	They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers.
Outcome:	The proposed model is based on human-verified QA pairs and contains 15K questions.

KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding (2023.findings-acl)

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Zijun Yao, Yantao Liu, Xin Lv, Shulin Cao, Jifan Yu, Juanzi Li, Lei Hou

Reasoning

Challenge:	Existing benchmarks for deep text understanding have encountered two major limitations . most require human annotation of knowledge, which leads to limited knowledge coverage .
Approach:	They propose a benchmark to help readers understand a document with prior knowledge . they use massive knowledge bases to guide annotators and large language models to construct knowledgable questions .
Outcome:	The proposed benchmarks have limited knowledge coverage and use choices or spans as answers, which results in narrow answer space.

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions (2023.findings-emnlp)

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Shulin Cao, Jiajie Zhang, Jiaxin Shi, Xin Lv, Zijun Yao, Qi Tian, Lei Hou, Juanzi Li

Reasoning

Challenge:	Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought reasoning.
Approach:	They propose a method to solve complex questions with a tree-of-thought approach using parametric knowledge and retrieved external knowledge to augment CoT reasoning.
Outcome:	The proposed approach outperforms SOTA methods on three Complex QA datasets under the open-domain setting.

Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models (2026.acl-long)

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Nianyi Lin, Jiajie Zhang, Lei Hou, Juanzi Li

Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)

Challenge:	Existing methods for reinforcement learning (RL) require a large sample size to be implemented.
Approach:	They propose a memory-efficient RL algorithm that maximizes a lower bound of the ELBO-based objective.
Outcome:	Experiments show that BGPO outperforms previous RL algorithms for diffusion large language models in math problem solving, code generation, and planning tasks.

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Yixin Cao, Xiang Ji, Xin Lv, Juanzi Li, Yonggang Wen, Hanwang Zhang

ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)

Challenge:	Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory .
Approach:	They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set.
Outcome:	The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu

Challenge:	Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates.
Approach:	They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion.
Outcome:	The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings.

Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (C18-1)

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Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong

CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)

Challenge:	Existing methods for identifying semantic type of entities are incomplete even in large knowledge bases.
Approach:	They propose an attributed and predictive entity embedding method which can fully utilize various kinds of information comprehensively.
Outcome:	Experiments on two real DBpedia datasets show that the proposed method outperforms 8 state-of-the-art methods with 4.0% improvement in Mi-F1 and 5.2% improvement in Ma-F1.

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Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou

Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)

Challenge:	Existing EE methods do not model event characteristics from large unsupervised data.
Approach:	They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures.
Outcome:	The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks.

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Mengna Zhu, Jibing Wu, Lihua Liu, Yuran Gong, Yang Hao, Fu Yachao, Mao Wang, Lei Hou, Juanzi Li

SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation (2025.acl-long)

Challenge:	Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research .
Approach:	They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability.
Outcome:	The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms.

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Zijun Yao, Weijian Qi, Liangming Pan, Shulin Cao, Linmei Hu, Liu Weichuan, Lei Hou, Juanzi Li

Information Retrieval

Challenge:	Adaptive Retrieval-Augmented Generation (RAG) is an effective strategy to alleviate hallucination of large language models (LLMs).
Approach:	They propose a novel adaptive RAG model that extracts self-aware uncertainty of large language models from their internal states and invokes retrieval accordingly.
Outcome:	The proposed model outperforms existing adaptive RAG methods on complex and simple Question Answering datasets.

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following (2025.emnlp-main)

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Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li

Challenge:	Best practices for RL in instruction following remain underexplored.
Approach:	They propose a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model.
Outcome:	The proposed method achieves state-of-the-art performance among models of comparable size and generalizes well to unseen constraints.

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models (2026.findings-acl)

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Yuhao Wu, Yushi Bai, Zhiqiang Hu, Juanzi Li, Roy Ka-Wei Lee

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Challenge:	Long-form text generation remains a challenge for large language models . generating extended sequences often leads to degraded coherence and logical consistency .
Approach:	They propose a framework that integrates explicit structured thinking into long-form text generation.
Outcome:	The proposed framework surpasses even larger-scale models in evaluation and human evaluation.

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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)

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Daniel Zhang-li, Jing Zhang, Jifan Yu, Xiaokang Zhang, Peng Zhang, Jie Tang, Juanzi Li

FFAEval: Evaluating Dialogue System via Free-For-All Ranking (2023.findings-emnlp)

Challenge:	Existing studies have explored the use of entity linking (EL) in downstream tasks.
Approach:	They propose a modularized entity linking toolkit for easy task adaptation.
Outcome:	The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods.

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Zeyao Ma, Zijun Yao, Jing Zhang, Jifan Yu, Xiaohan Zhang, Juanzi Li, Jie Tang

Natural Language Interfaces

Challenge:	Existing evaluation metrics for open-domain dialogue systems show poor correlation with human assessment.
Approach:	They propose a free-for-all human evaluation framework that shares dialogue history with annotators for multi-turn scoring.
Outcome:	The proposed framework achieves a strong correlation with human assessment on English and Chinese dialogue systems.

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)

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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)

Challenge:	Massive open online courses (MOOCs) are a popular educational platform for advanced research.
Approach:	They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource.
Outcome:	The proposed datasets show that they can facilitate research in MOOCs.

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Xin Lv, Lei Hou, Juanzi Li, Zhiyuan Liu

Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)

Challenge:	Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance.
Approach:	They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances.
Outcome:	The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset.

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Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu

Structured Data in NLP

Challenge:	Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult.
Approach:	They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses .
Outcome:	The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata.

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)

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Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model (D19-1)

Challenge:	Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions.
Approach:	They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks.
Outcome:	The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude.

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Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

Multi-Channel Graph Neural Network for Entity Alignment (P19-1)

Challenge:	Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages.
Approach:	They propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model to make better use of seed alignments to propagate over the entire graphs with KG-based constraints.
Outcome:	The proposed method can make better use of seed alignments to propagate over entire graphs with KG-based constraints.

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Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua

Structured Data in NLP

Challenge:	Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments.
Approach:	They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels.
Outcome:	The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments.

TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)

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Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Z. Pan, Jiaoyan Chen, Freddy Lecue

ADELIE: Aligning Large Language Models on Information Extraction (2024.emnlp-main)

Challenge:	Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy .
Approach:	They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method .
Outcome:	The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance.

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Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li

Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)

Challenge:	Large language models (LLMs) struggle to follow complex instructions of IE tasks due to not being aligned with humans.
Approach:	They propose an aligned large language moDEL that effectively solves various IE tasks including closed IE, open IE and on-demand IE.
Outcome:	The proposed model achieves state-of-the-art (SoTA) performance among open-source models.

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Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua

Structured Data in NLP

Challenge:	Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs.
Approach:	They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently.
Outcome:	The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets.

On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)

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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering (2023.acl-long)

Challenge:	Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing.
Approach:	They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability.
Outcome:	The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing.

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Jiajie Zhang, Shulin Cao, Tingjian Zhang, Xin Lv, Juanzi Li, Lei Hou, Jiaxin Shi, Qi Tian

Question Answering

Challenge:	Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. Existing work in XQA focuses on integrating information from heterogeneous knowledge sources.
Approach:	They propose to leverage question decomposing for heterogeneous knowledge integration by breaking down a complex question into simpler ones and selecting the appropriate knowledge source for each sub-question.
Outcome:	The proposed framework outperforms SOTA methods on complex QA datasets.

How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering (2024.findings-acl)

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Jinxin Liu, Shulin Cao, Jiaxin Shi, Tingjian Zhang, Lunyiu Nie, Linmei Hu, Lei Hou, Juanzi Li

The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)

Challenge:	Recent studies have validated that large language models (LLMs) are capable of solving some KBQA problems, but there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing.
Approach:	They propose to evaluate the understanding and generation ability of large language models (LLMs) to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs.
Outcome:	The proposed model can understand formal languages as well as humans, but generating correct logical forms remains a challenge.

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Hao Peng, Xiaozhi Wang, Feng Yao, Kaisheng Zeng, Lei Hou, Juanzi Li, Zhiyuan Liu, Weixing Shen

DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)

Challenge:	Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts.
Approach:	They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls.
Outcome:	The results show that the evaluations are reliable and lack pipeline evaluations.

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MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, Juanzi Li

Neural Collective Entity Linking (C18-1)

Challenge:	Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets.
Approach:	They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments.
Outcome:	The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings.

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Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu

Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)

Challenge:	Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently.
Approach:	They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency.
Outcome:	The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias.

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Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, Lei Hou, Zhiyuan Liu, Juanzi Li

DocEE-zh: A Fine-grained Benchmark for Chinese Document-level Event Extraction (2024.findings-emnlp)

Challenge:	Pre-trained language models have demonstrated superior performance on various natural language processing tasks.
Approach:	They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training .
Outcome:	The neurons are highly predictive of task labels after prompt tuning for specific tasks.

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Minghui Liu, MeiHan Tong, Yangda Peng, Lei Hou, Juanzi Li, Bin Xu

Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking (2024.acl-long)

Challenge:	Chinese document-level event extraction is still largely unexplored.
Approach:	They propose a Chinese document-level event extraction dataset with over 36,000 events and 210,000 arguments.
Outcome:	The proposed dataset includes over 36,000 events and more than 210,000 arguments . it is an extension of the DocEE dataset, utilizing the same event schema and annotated by human experts.

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Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, Jie Tang

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)

Challenge:	Existing factuality detection methods are not effective for large language models (LLMs).
Approach:	They propose a probing model that trains on offline consistency checking results.
Outcome:	The proposed model reduces the computational burden of generating multiple responses by online consistency verification and improves on factuality detection and question answering benchmarks.

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Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)

Challenge:	Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable .
Approach:	They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark .
Outcome:	The proposed framework outperforms existing models in terms of performance and interpretability.

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Xin Lv, Yankai Lin, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou

LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)

Challenge:	Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance .
Approach:	They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences .
Outcome:	The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs.

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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun

Pre-training Distillation for Large Language Models: A Design Space Exploration (2025.acl-long)

Challenge:	Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data.
Approach:	They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications .
Outcome:	The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval.

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Hao Peng, Xin Lv, Yushi Bai, Zijun Yao, Jiajie Zhang, Lei Hou, Juanzi Li

GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation (2022.emnlp-main)

Challenge:	Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model for model compression.
Approach:	They extend knowledge distillation to the pre-training phase of large language models . they first conduct an experiment using a teacher LLM to distill a 1.9B student LLM .
Outcome:	The proposed model can be used to distill a 1.9B student model using a teacher LLM.

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Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun, Qi Tian, Lei Hou, Juanzi Li, Jidong Zhai

Syntactically Robust Training on Partially-Observed Data for Open Information Extraction (2022.findings-emnlp)

Challenge:	Existing approaches to neural semantic parsing are limited by the semantic gap between natural and formal languages.
Approach:	They propose a unified intermediate representation for graph query languages, named GraphQ IR, which has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure.
Outcome:	The proposed representation can convert user queries into graphQ IR, which can later be losslessly compiled into various downstream graph query languages.

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Ji Qi, Yuxiang Chen, Lei Hou, Juanzi Li, Bin Xu

Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)

Challenge:	Open Information Extraction models have shown promising results with sufficient supervision, but the syntactic distribution of training data is partially observable in comparison to the real world.
Approach:	They propose a syntactically robust training framework that enables models to be trained on a multi-paraphrase distribution based on diverse paraphrase generation.
Outcome:	The proposed framework can be applied to other syntactic partial observable domains.

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Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim

HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)

Challenge:	Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning.
Approach:	They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling.
Outcome:	The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios.

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Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren

SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning (2022.emnlp-main)

Challenge:	Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles.
Approach:	They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles.
Outcome:	The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy.

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Yushi Bai, Xin Lv, Juanzi Li, Lei Hou, Yincen Qu, Zelin Dai, Feiyu Xiong

MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification (2024.findings-emnlp)

Challenge:	Existing methods for multi-hop knowledge graph reasoning suffer from slow and poor convergence . a transformer model can be used to learn and predict in an end-to-end fashion, giving faster convergence compared to previous methods .
Approach:	They propose a Sequence-to-sequence based multi-hop reasoning framework . it uses an encoder-decoder transformer structure to translate the query to a path .
Outcome:	The proposed framework can learn and predict in an end-to-end fashion, which gives better and faster convergence.

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Kai Sun, Yushi Bai, Ji Qi, Lei Hou, Juanzi Li

Information Retrieval

Challenge:	Existing benchmarks for multimodal reasoning in large multimodal models are underperforming on multimodal tasks.
Approach:	They propose a benchmark for multimodal reasoning in large multimodal models, MM-MATH . MM's process evaluation employs LMM-as-a-judge to automatically analyze solution steps . diagram misinterpretation is the most common error, they find .
Outcome:	The proposed model achieves only 31% accuracy, compared to 82% for humans.

Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (D19-1)

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Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong

TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (2021.emnlp-main)

Challenge:	Existing methods for inferring the fine-grained type of an entity from knowledge base are incomplete and lack type information.
Approach:	They propose a novel Deep Learning architecture to infer the fine-grained type of an entity from a knowledge base.
Outcome:	The proposed method significantly outperforms four state-of-the-art methods on two large-scale datasets.

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Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, Hanwang Zhang

Explainability & Interpretability in NLP

Challenge:	Existing models infer the answer by predicting the sequential relation path or aggregating the hidden graph features.
Approach:	They propose a model which jumps between entities at multiple steps . they demonstrate that TransferNet surpasses state-of-the-art models by a large margin .
Outcome:	The proposed model surpasses state-of-the-art models on MetaQA and on other datasets.

Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li

TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)

Challenge:	Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach:	They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome:	The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang

Multimodality

Challenge:	TableLLM is a robust large language model capable of handling tabular data manipulation tasks.
Approach:	They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy.
Outcome:	The proposed model has 8 billion parameters and is capable of handling tabular data tasks.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)

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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li

Multilinguality

Challenge:	Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases.
Approach:	They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities.
Outcome:	The proposed model outperforms open-source models but struggles on longer contexts.

LongAlign: A Recipe for Long Context Alignment of Large Language Models (2024.findings-emnlp)

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Yushi Bai, Xin Lv, Jiajie Zhang, Yuze He, Ji Qi, Lei Hou, Jie Tang, Yuxiao Dong, Juanzi Li

Dependency Parsing via Sequence Generation (2022.findings-emnlp)

Challenge:	Existing studies to build long context language models focus on context extension and continual training on long text.
Approach:	They propose a recipe for instruction fine-tuning on input sequences of similar length . they adopt packing and sorted batching strategies to speed up supervised fine-uning .
Outcome:	The proposed model outperforms existing recipes for LLMs in long context tasks by 30% while maintaining proficiency in handling short, generic tasks.

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Boda Lin, Zijun Yao, Jiaxin Shi, Shulin Cao, Binghao Tang, Si Li, Yong Luo, Juanzi Li, Lei Hou

Syntactic Text Processing

Challenge:	Existing methods for dependency parsing are transition-based, graph-based and sequence-to-sequence method.
Approach:	They propose to achieve dependency parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures.
Outcome:	The proposed method performs well on DP benchmarks including PTB, UD2.2, SDP15 and SemEval16.

UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)

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Shangqing Tu, Jifan Yu, Fangwei Zhu, Juanzi Li, Lei Hou, Jian-Yun Nie

Low-Resource NLP

Challenge:	Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract.
Approach:	They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience.
Outcome:	The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation.

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition (2021.acl-long)

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Meihan Tong, Shuai Wang, Bin Xu, Yixin Cao, Minghui Liu, Lei Hou, Juanzi Li

Low-Resource NLP

Challenge:	Existing methods to classify named entity mentions with fewshots fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario.
Approach:	They propose a model that can automatically induce different unde- fined classes from the other class to improve few-shot Named Entity Recognition (NER) .
Outcome:	The proposed model outperforms five state-of-the-art models in 1- shot and 5-shots settings on four NER bench marks.

From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)

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Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)

Challenge:	Large language models produce content lacking pedagogical depth when asked to generate lessons .
Approach:	They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications.
Outcome:	The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines.

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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li

Multimodality

Challenge:	Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) .
Approach:	They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries.
Outcome:	The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.

LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation (2024.emnlp-demo)

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Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Yan Xuan, Jiaxi Yuan, Bin Xu, Juanzi Li, Zhiyuan Liu

MAVEN-FACT: A Large-scale Event Factuality Detection Dataset (2024.findings-emnlp)

Challenge:	Semi-structured interviews are a crucial method of data acquisition in qualitative research.
Approach:	They propose a semi-structured interview system that automates interview preparation, analysis and control by interviewers.
Outcome:	Experimental results show that LM-Interview performs comparable to human interviewers . the system can be used to analyze semi-structured interviews without interviewers' involvement .

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Chunyang Li, Hao Peng, Xiaozhi Wang, Yunjia Qi, Lei Hou, Bin Xu, Juanzi Li

Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards (2026.acl-long)

Challenge:	Event factuality detection is under-explored due to the lack of high-quality large-scale data . efd is a subfield of event understanding, which aims to determine the factuity of textual events.
Approach:	They propose a large-scale EFD dataset with factuality annotations of 112,276 events . they find that adopting event arguments and relations helps in event factuity detection .
Outcome:	The proposed dataset includes factuality annotations of 112,276 events . it is the largest EFD dataset and is challenging for fine-tuned models and large language models .

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Jiajie Zhang, Xin Lv, Ling Feng, Lei Hou, Juanzi Li

KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base (2022.acl-long)

Challenge:	Existing methods for reinforcement learning (RL) rely on binary outcome rewards that fail to capture the comprehensiveness and factuality of agents’ reasoning process.
Approach:	They propose a reward framework that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity.
Outcome:	The proposed framework outperforms standard outcome-based RL baselines across multiple deep search benchmarks and shows that it discourages shortcut exploitation and promotes comprehensive, evidence-grounded reasoning.

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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang

Natural Language Interfaces

Challenge:	Existing benchmarks for Complex KBQA lack compositional reasoning capabilities . Existing methods for Complex questions are poor in diversity or scale .
Approach:	They propose a compositional programming language to represent the reasoning process of complex questions.
Outcome:	The proposed dataset includes around 120K diverse natural language questions . it provides a compositional and interpretable programming language to represent the reasoning process of complex questions based on the proposed model .

Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2025.acl-long)

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Kejian Zhu, Shangqing Tu, Zhuoran Jin, Lei Hou, Juanzi Li, Jun Zhao

OpenKE: An Open Toolkit for Knowledge Embedding (D18-2)

Challenge:	Recent studies have focused on building dynamic benchmarks to address data contamination issues.
Approach:	They propose a method for identifying shortcut neurons through comparative and causal analysis to suppress shortcut neurons.
Outcome:	The proposed method overestimates contaminated models and is highly generalizable across benchmarks and hyperparameter settings.

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Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, Juanzi Li

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)

Challenge:	Existing knowledge embedding tools are available for embeddable knowledge graphs.
Approach:	They propose a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
Outcome:	The toolkit and pre-trained embeddings are available on http://openke.thunlp.org/.

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Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems (2025.acl-long)

Challenge:	Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance.
Approach:	They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results.
Outcome:	The proposed method improves on the datasets from Coursera and XuetangX.

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Hao Peng, Yunjia Qi, Xiaozhi Wang, Zijun Yao, Bin Xu, Lei Hou, Juanzi Li

Challenge:	Existing reward models focus on human preferences, neglecting verifiable correctness signals.
Approach:	They propose a reward system that combines human preference rewards with verifiable correctness signals to provide reliable rewards.
Outcome:	The proposed reward agent significantly outperforms vanilla reward models on benchmarks and inference-time best-of-n searches on real-world tasks.

MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)

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Yong Guan, Hao Peng, Lei Hou, Juanzi Li

DeepPrune: Parallel Scaling without Inter-trace Redundancy (2026.findings-acl)

Challenge:	Existing research indicates that LLMs can be overconfident and stubborn.
Approach:	They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate.
Outcome:	The proposed approach outperforms established baselines on various ERE tasks and LLMs.

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Shangqing Tu, Yaxuan Li, Yushi Bai, Lei Hou, Juanzi Li

Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models (2024.lrec-main)

Challenge:	Parallel scaling is a powerful paradigm to enhance reasoning capabilities in large language models.
Approach:	They propose a framework that enables efficient parallel scaling through dynamic pruning.
Outcome:	The proposed framework achieves token reductions of 65.73% to 88.50% compared to consensus sampling while maintaining competitive accuracy within 3.4 percentage points.

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Yantao Liu, Zijun Yao, Xin Lv, Yuchen Fan, Shulin Cao, Jifan Yu, Lei Hou, Juanzi Li

LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)

Challenge:	Existing studies have explained to what extent LLMs extract conflicting knowledge from the provided text, but they neglect the necessity to reason with conflicting information.
Approach:	They construct a dataset for knowledge conflict resolution examination in the form of question answering that divides reasoning with conflicting knowledge into three levels.
Outcome:	The proposed dataset enables analysis of reasoning with conflicting knowledge in the form of question answering.

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Mengna Zhu, Kaisheng Zeng, JibingWu JibingWu, Lihua Liu, Hongbin Huang, Lei Hou, Juanzi Li

Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)

Challenge:	Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging.
Approach:	They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system.
Outcome:	The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance.

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Fangwei Zhu, Jifan Yu, Hailong Jin, Lei Hou, Juanzi Li, Zhifang Sui