Papers by Lei Shen

Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)

Challenge:	Recent studies show query expansions generate hypothetical documents that answer queries as expansions.
Approach:	They propose a corpus-steered query expansion to promote incorporation of knowledge embedded within the corpus.
Outcome:	et al. analyzed corpus-based Query Expansion (CSQE) using LLMs to generate hypothetical documents that answer the query.

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Zhenyu Zhang, Lei Shen, Yuming Zhao, Meng Chen, Xiaodong He

Automatic Table Union Search with Tabular Representation Learning (2023.findings-acl)

Challenge:	a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks .
Approach:	They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively .
Outcome:	The proposed method outperforms existing models and achieves a 3.3% improvement on average.

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Xuming Hu, Shen Wang, Xiao Qin, Chuan Lei, Zhengyuan Shen, Christos Faloutsos, Asterios Katsifodimos, George Karypis, Lijie Wen, Philip S. Yu

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)

Challenge:	Existing methods to identify uniability based on column representations are insufficient to reveal latent relational features to describe column relation between pair of columns.
Approach:	They propose a self-supervised table union search framework called AutoTUS to learn column relational representations in a multi-stage manner.
Outcome:	The proposed framework improves on the SOTA baseline and on real-world datasets.

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Lei Liu, Hao Zhu, Xiaoyan Yang, Yue Shen, Zhixuan Chu, Jian Wang, Jinjie Gu, Kui Ren

A Group Fairness Lens for Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare.
Approach:	They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss.
Outcome:	Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption.

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Guanqun Bi, Yuqiang Xie, Lei Shen, Yanan Cao

Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods focusing on a few groups lack a comprehensive categorical perspective to evaluate LLMs’ potential biases and unfairness.
Approach:	They propose to evaluate LLM biases from a group fairness lens using a hierarchical schema characterizing diverse social groups.
Outcome:	The proposed method mitigates biases in LLMs from a group fairness lens and encapsulates target-attribute combinations across multiple dimensions.

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Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Fei Huang, Ya-Qin Zhang, Yang Liu

Challenge:	Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited.
Approach:	They propose an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT.
Outcome:	Experiments on 7B-scale writer models show that Writing-RL improves long-form writing performance over strong SFT baselines.

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2020.acl-main)

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Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara Berg, Mohit Bansal

Visual Data in NLP

Challenge:	Existing methods for generating paragraph descriptions for videos require a coherent paragraph and a higher level of coherence.
Approach:	They propose a new method that generates a summarized memory state from video segments and sentence history to help better predict the next sentence.
Outcome:	The proposed method generates more coherent and less repetitive paragraph captions while maintaining relevance to the input video events.

SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration (2024.emnlp-industry)

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Yuanhao Shen, Xiaodan Zhu, Lei Chen

Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have a profound impact on a wide range of applications.
Approach:	They propose a framework to mitigate the tool-abuse behavior of Large Language Models and propose SMARTCAL to mitigate this issue.
Outcome:	The proposed framework improves the performance of LLMs on three datasets with two mainstream tool-use frameworks and shows an 8.6% increase in QA performance and 21.6 percent lower expected calibration error (ECE) than existing methods.

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Lei Shen, Xiaoyu Shen

Challenge:	Auto-SLURP is a benchmark dataset for evaluating multi-agent frameworks powered by large language models.
Approach:	Auto-SLURP is a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks . authors propose it extends original SLURP dataset by relabeling data and integrating simulated servers and external services.
Outcome:	The proposed dataset extends the original SLURP dataset for natural language understanding tasks.

Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)

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Yiwei Wei, Shaozu Yuan, Ruosong Yang, Lei Shen, Zhangmeizhi Li, Longbiao Wang, Meng Chen

Multimodality

Challenge:	Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity.
Approach:	They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space.
Outcome:	The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets.

RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)

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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou

Representation Learning

Challenge:	Traditional metrics for automatic text evaluation are tailored to specific tasks, while LLM-based evaluation metrics are costly.
Approach:	They propose a metric that leverages projections of LLM representations for evaluation.
Outcome:	The proposed metric exhibits higher correlation with human judgments than previous methods on 14 datasets.

ThinkQE: Query Expansion via an Evolving Thinking Process (2025.findings-emnlp)

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Yibin Lei, Tao Shen, Andrew Yates

Information Retrieval

Challenge:	LLM-based methods often generate narrowly focused expansions that overlook these desiderata.
Approach:	They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions .
Outcome:	The proposed framework outperforms prior approaches on diverse web search benchmarks.

CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation (2020.acl-main)

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Lei Shen, Yang Feng

Text Generation

Challenge:	Existing methods for generating emotion-controllable response are inadequate due to content consistency and lack of coherence.
Approach:	They propose a framework that extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively.
Outcome:	The proposed framework outperforms baseline models in coherence, diversity, and relation to emotion factors.

Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)

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Lei Shen, Jinchao Zhang, Jiao Ou, Xiaofang Zhao, Jie Zhou

From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)

Challenge:	Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response.
Approach:	They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples.
Outcome:	The proposed model outperforms baseline models in producing coherent and empathetic responses.

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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu

Low-Resource NLP

Challenge:	Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning.
Approach:	They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model.
Outcome:	The proposed method improves egocentric reasoning abilities on six tasks.

Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)

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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)

Challenge:	Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance.
Approach:	They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs.
Outcome:	The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance.

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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen

Challenge:	Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines.
Approach:	They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions .
Outcome:	The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3.

Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)

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Yan Lei, Liang Pang, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng

DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

Challenge:	Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors.
Approach:	They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires.
Outcome:	The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms.

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Guanqun Bi, Lei Shen, Yanan Cao, Meng Chen, Yuqiang Xie, Zheng Lin, Xiaodong He

Text Generation

Challenge:	Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach:	They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome:	The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)

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Ruixue Liu, Shaozu Yuan, Aijun Dai, Lei Shen, Tiangang Zhu, Meng Chen, Xiaodong He

GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)

Challenge:	Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks.
Approach:	They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese.
Outcome:	The proposed model improves the few-shot table understanding in Chinese.

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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun

Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)

Challenge:	Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically.
Approach:	They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance.
Outcome:	The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs.

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Lei Gao, Xinnan Zhang, Xian Wu, Shen Ge, Yefeng Zheng

Structured Data in NLP

Challenge:	Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue.
Approach:	They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items .
Outcome:	The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set.

Enhancing Lexicon-Based Text Embeddings with Large Language Models (2025.acl-long)

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Yibin Lei, Tao Shen, Yu Cao, Andrew Yates

DT-QDC: A Dataset for Question Comprehension in Online Test (2020.coling-main)

Challenge:	Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks.
Approach:	They introduce the first lexicon-based embeddings that consolidates the vocabulary space through token embeddation clustering to handle the issue of token redundancy in LLM vocabularies.
Outcome:	The proposed model outperforms dense embeddings on the Massive Text Embedding Benchmark (MTEB) it also supports efficient dimension pruning without any specialized objectives like Matryoshka Representation Learning.

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Sijin Wu, Yujiu Yang, Nicholas Yung, Zhengchen Shen, Zeyang Lei

Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)

Challenge:	Existing methods to measure difficulty of questions are not accurate enough to guide learning.
Approach:	They propose to use a Chinese DT-QDC dataset to measure difficulty of questions and provide a new model that can improve the judgment of difficulty from different perspectives.
Outcome:	The proposed methods outperform baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE, and 28.2% on MSC on the new DT-QDC dataset.

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Yi Bin, Mengqun Han, Wenhao Shi, Lei Wang, Yang Yang, See-Kiong Ng, Heng Shen

Numerical Reasoning

Challenge:	Existing MWP solvers do not handle variants that can be derived via mathematical manipulation.
Approach:	They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description.
Outcome:	The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS.

OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)

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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun

Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge (C18-1)

Challenge:	Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks.
Approach:	They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions.
Outcome:	The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.

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Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Question Answering

Challenge:	Existing approaches to answer selection are limited in domains with limited labeled data.
Approach:	They propose a Knowledge-aware Attentive Network framework for cross-domain answer selection that uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domain.
Outcome:	The proposed model outperforms strong competitors by a noticeable margin in cross-domain answer selection.

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

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

Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)

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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Qiao Liang, Yuke Zhu, Chao Ge, Lei Yang, Ying Shen, Bo Zheng, Sheng Guo

Reasoning

Challenge:	Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment.
Approach:	They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment.
Outcome:	The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency.

Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)

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Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates

Differentially Private Natural Language Models: Recent Advances and Future Directions (2024.findings-eacl)

Challenge:	Existing methods for large language modeling are based on task-related instructions or prompts.
Approach:	They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts.
Outcome:	The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts .

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Lijie Hu, Ivan Habernal, Lei Shen, Di Wang

Responsible & Trustworthy NLP

Challenge:	Recent advances in deep learning have led to great success in various natural language processing tasks.
Approach:	They propose a systematic review of recent advances in DP deep learning models . they discuss some differences and additional challenges of DP-NLP .
Outcome:	The proposed method can prevent reconstruction attacks and protect against potential side knowledge while maintaining the privacy of sensitive data.

Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)

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Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, Chunxu Shen

From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)

Challenge:	Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB.
Approach:	They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities .
Outcome:	The proposed framework can achieve superior performance over the state of the arts.

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Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi, Zhenyu Lei, Cong Shen, Jundong Li

Low-Resource NLP

Challenge:	In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples.
Approach:	They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling.
Outcome:	The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances.

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

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

Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)

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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Kai Lei, Daoyuan Chen, Yaliang Li, Nan Du, Min Yang, Wei Fan, Ying Shen

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

Challenge:	Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance.
Approach:	They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning.
Outcome:	The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.

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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

Responsible & Trustworthy NLP

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models (2022.emnlp-main)

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Bowen Shen, Zheng Lin, Yuanxin Liu, Zhengxiao Liu, Lei Wang, Weiping Wang

Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents (2020.acl-main)

Challenge:	Existing statically compressed pre-trained language models lack spatial and temporal efficiency due to their large size and wide width.
Approach:	They propose a spatially and temporally efficient model which retains the major capacity of PLMs.
Outcome:	The proposed model retains the major capacity of pre-trained language models at high compression and acceleration rate with 1/8 parameters and 1/19 FLOPs of BERT.

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Daoyuan Chen, Yaliang Li, Kai Lei, Ying Shen

Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)

Challenge:	Existing methods for entity and relation extraction require light human annotation efforts.
Approach:	They propose a method to re-label noisy instances with a cooperative group . they use a confidence consensus module to gather the wisdom of all agents .
Outcome:	The proposed model outperforms state-of-the-art methods on two real-world datasets.

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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang

Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards (2026.acl-long)

Challenge:	Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue.
Approach:	They propose to use a large language model (LLM) to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates.
Outcome:	The proposed method breaks brute-force combinations of retrievers with LLMs and lifts the performance of zero-shot retrieval to be very competitive on benchmark datasets.

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Yuanjie Lyu, Chengyu Wang, Lei Shen, Jun Huang, Tong Xu

GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)

Challenge:	Existing agentic training data are narrow in task variety and easily solved . real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes.
Approach:	They propose a framework that synthesizes diverse tool-use training data and simulates complete environments.
Outcome:	The proposed framework synthesizes diverse tool-use training data and simulates complete environments.

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Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou

Natural Language Interfaces

Challenge:	Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.
Approach:	They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings.
Outcome:	The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines.

Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)

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Wen Zhang, Liang Huang, Yang Feng, Lei Shen, Qun Liu

Machine Translation

Challenge:	Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study .
Approach:	They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up.
Outcome:	The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search.

DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)

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Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

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

Challenge:	Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships.
Approach:	They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions.
Outcome:	The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions.

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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

Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)

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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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

Challenge:	Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem.
Approach:	They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces.
Outcome:	The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems.

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

Reasoning

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Reasoning

Challenge:	Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations.
Approach:	They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge.
Outcome:	The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld.

Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables (P19-1)

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Lei Shen, Yang Feng, Haolan Zhan

DiscoverGPT: Multi-task Fine-tuning Large Language Model for Related Table Discovery (2025.findings-naacl)

Challenge:	Existing work on multi-turn conversations has focused on the relationship between the response and context, but it is lacking a model to model the relationship.
Approach:	They propose a conversational semantic relationship RNN model to construct hierarchical dependency between utterances and their context.
Outcome:	The proposed model significantly improves the quality of responses in terms of fluency, coherence, and diversity compared to baseline methods.

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Xuming Hu, Xiao Qin, Chuan Lei, Asterios Katsifodimos, Zhengyuan Shen, Balasubramaniam Srinivasan, Huzefa Rangwala

CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation (2021.emnlp-main)

Challenge:	Existing methods to learn and evaluate the table semantic relatedness of tabular data are based on pretrain-and-finetune paradigms.
Approach:	They propose a multi-task fine-tuning framework that holistically discovers and leverages the intricate relationships among the supervisions to optimize the performance on the data discovery task.
Outcome:	The proposed framework outperforms the best performing baseline by up to 7% in F1 score.

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Haolan Zhan, Lei Shen, Hongshen Chen, Hainan Zhang