Papers by Yin Luo

Iterative Dual Domain Adaptation for Neural Machine Translation (D19-1)

Challenge:	achieving data-efficient post-training of Large Language Models is a key research question.
Approach:	They propose a taxonomy of data-efficient LLM post-training methods from a data-centric perspective.
Outcome:	The proposed methods cover data selection, data quality enhancement, synthetic data generation, data distillation and compression, and self-evolving data ecosystems.

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Jiali Zeng, Yang Liu, Jinsong Su, Yubing Ge, Yaojie Lu, Yongjing Yin, Jiebo Luo

Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (P19-1)

Challenge:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our proposed framework.
Approach:	They propose an iterative dual domain adaptation framework for neural machine translation that uses multiple corpora to perform bidirectional translation knowledge transfer.
Outcome:	Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of the proposed framework.

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Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, Weihua Luo

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)

Challenge:	Abstractive Sentence Summarization (ASSUM) is a monolingual task that focuses on grasping the core idea of the source sentence and presenting it as the summary.
Approach:	They propose to use monolingual ASSUM to train a cross-lingual ASL system . they propose to train the system on summary word generation and attention .
Outcome:	Experiments show that the proposed method improves on the monolingual ASSUM task.

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Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (2022.naacl-main)

Challenge:	Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples .
Approach:	They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE.
Outcome:	The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR .

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Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Despite advances in self-supervised learning, there is a lack of models that can effectively capture both intra- and intra-item semantics for semi-structured session data.
Approach:	They propose a graph-based transformer model for semi-structured session data that captures both intra- and intra-item semantics.
Outcome:	The proposed model outperforms baselines in three session search and entity linking tasks by up to 9%.

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

PromISe: Releasing the Capabilities of LLMs with Prompt Introspective Search (2024.lrec-main)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Minzheng Wang, Nan Xu, Jiahao Zhao, Yin Luo, Wenji Mao

Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)

Challenge:	Existing evaluation benchmarks for large language models use uniform manual prompts, resulting in underestimation of performance.
Approach:	They propose a prompt introspective search framework that integrates self-introspect and self-refine to unlock the capabilities of LLMs.
Outcome:	The proposed framework significantly boosts the performance of 12 well-known LLMs compared to baseline methods.

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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin

Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)

Challenge:	Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers.
Approach:	They constructed a dataset which contains a corresponding reference summary for each original lengthy answer.
Outcome:	The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset.

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Qianglong Chen, Guohai Xu, Ming Yan, Ji Zhang, Fei Huang, Luo Si, Yin Zhang

Let’s Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models (2025.findings-acl)

Challenge:	Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach:	They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome:	The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Kangyang Luo, Zichen Ding, Zhenmin Weng, Lingfeng Qiao, Meng Zhao, Xiang Li, Di Yin, Jinlong Shu

A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation (2020.acl-main)

Challenge:	Existing efforts to improve CoT prompting have limitations that require extensive human effort or performance needs to be improved.
Approach:	They propose a prompt approach for automatic reasoning called LBS3 inspired by curriculum learning which better reflects human learning habits.
Outcome:	The proposed approach achieves strongly competitive performance compared to baselines in reasoning-intensive tasks with varying open- and closed-source LLMs.

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Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, Jiebo Luo

Multimodality

Challenge:	Existing multi-modal neural machine translation models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities.
Approach:	They propose a graph-based multi-modal fusion encoder that exploits fine-grained semantic correspondences between different modalities.
Outcome:	The proposed encoder significantly extends the conventional text-based translation by taking images as additional inputs.

From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base (2023.emnlp-main)

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Wangzhen Guo, Linyin Luo, Hanjiang Lai, Jian Yin

Dual Complex Number Knowledge Graph Embeddings (2024.lrec-main)

Challenge:	Existing methods for parsing knowledge-base questions into executable logical forms have not been successful on complex KBQA.
Approach:	They propose a new semantic parser called KoPL to model the reasoning processes . they propose 'parse-execute-refine' paradigm to unlock reasoning ability .
Outcome:	The proposed parser performs better than the state-of-the-art on complex KBQA . the proposed parsed-execute-refine paradigm can model complex reasoning steps .

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Yao Dong, Qingchao Kong, Lei Wang, Yin Luo

Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)

Challenge:	Existing knowledge graph embedding methods fail to model non-commutative composition patterns . extending to such sophisticated spaces increases the amount of parameters, which greatly reduces the parameter efficiency.
Approach:	They propose a new knowledge graph embedding method that maps entities to the dual complex number space and represents relations as rotations in 2D space via dual complex multiplication.
Outcome:	Experiments on multiple benchmark knowledge graphs show that the proposed method improves link prediction and path query answering.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options.
Approach:	They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations .
Outcome:	The proposed model outperforms human experts in multiple medical tasks.

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Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu

Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach (2025.findings-emnlp)

Challenge:	RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning.
Approach:	They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination.
Outcome:	The proposed model outperforms baselines and can transfer well to different retrievers.

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Xiaoran Yin, Xu Luo, Hao Wu, Lianli Gao, Jingkuan Song

Challenge:	Current approaches to automating complex tasks focus on reactive policies and focus on visual observations.
Approach:	They propose a framework that prioritizes natural language understanding and structured reasoning to enhance the agent’s global understanding of the environment by developing a task-oriented, refinable world model at the outset of the task.
Outcome:	The proposed framework outperforms existing approaches in simulated environments and on real mobile devices.

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Green & Sustainable NLP

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG (2026.acl-long)

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Chuang Zhou, Zheng Yuan, Linhao Luo, Zhaozhuo Xu, Yilin Xiao, Junnan Dong, Siyu An, di Yin, Xing Sun, Xiao Huang

StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) has been used for enhancing large language models with external knowledge.
Approach:	They propose a framework for mining efficient graph structures via hashing to enhance RAG . they adopt an inductive paradigm where global graph structure emerges from local hash collisions .
Outcome:	The proposed framework outperforms existing baselines while requiring no GPU resources or token budget.

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Yang Luo, Liu Xinran, TianTian Ji, Zhiyi Yin, Lingyun Peng, Shuyu Li

Robustness in NLP

Challenge:	Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored.
Approach:	They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails.
Outcome:	The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails.

Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)

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Xiangyu Duan, Hongfei Yu, Mingming Yin, Min Zhang, Weihua Luo, Yue Zhang

Summarization

Challenge:	Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora.
Approach:	They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task.
Outcome:	The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task.

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song

SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation (2025.emnlp-main)

Challenge:	Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts.
Approach:	They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce.
Outcome:	The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms.

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Zhiyuan Peng, Xin Yin, Rui Qian, Peiqin Lin, YongKang Liu, Hao Zhang, Chenhao Ying, Yuan Luo

MAR: Matching-Augmented Reasoning for Enhancing Visual-based Entity Question Answering (2024.emnlp-main)

Challenge:	Existing methods focus on Python and Java, neglecting Solidity, the programming language for Ethereum smart contracts.
Approach:	They construct a repository-level benchmark for Solidity to evaluate the performance of LLMs on Ethereum.
Outcome:	The proposed benchmarks show that the best performing LLM achieves only 26.29% Pass@10, highlighting room for improvement in Solidity code generation.

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Zhengxuan Zhang, Yin Wu, Yuyu Luo, Nan Tang

Multimodality

Challenge:	Multimodal large language models (MLLMs) struggle with visual-based entity questions (VEQA) MLLM can identify A, but may refrain from answering due to privacy concerns.
Approach:	They propose a method that uses vector representations to analyze visual-based entity questions (VEQA) they use visual cues and textual information to integrate visual cus and visual information .
Outcome:	The proposed method significantly improves visual-based entity question answering (VEQA) it can identify faces, names, and alignments within visual objects, and then derive the answer over this matching graph.

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song

Hierarchical Pretraining on Multimodal Electronic Health Records (2023.emnlp-main)

Challenge:	Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used.
Approach:	They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark.
Outcome:	The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history.

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Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, Fenglong Ma

ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)

Challenge:	Existing pretraining models on EHR data are too specific, limiting their transferability.
Approach:	They propose a general, unified pretraining framework for hierarchically multimodal EHR data that can be used to train models on a large dataset before fine-tuning it on 'upstream' tasks.
Outcome:	The proposed model performs on eight downstream tasks spanning three levels and compares with baselines on 18 different tasks.

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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang