Papers by Xin Yin

Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)

Challenge:	Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements.
Approach:	They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention.
Outcome:	The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins.

Copied to clipboard

Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin

Responsible & Trustworthy NLP

Challenge:	Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning.
Approach:	They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains.
Outcome:	Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE.

RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026.findings-acl)

Copied to clipboard

Xin Yin, Zixiang Ding, Yiang Zhang, Qiang Wang, Rui Wang, Chao Ni, Zhe Cui

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have strong performance on code translation tasks, but they struggle with repository-level scenarios where context is extensive and interdependent.
Approach:	They propose a framework that integrates retrieval with learning budget allocation for fine-grained context compression.
Outcome:	The proposed framework outperforms baselines on SWE-QA, CoderEval, and LongCodeU.

Copied to clipboard

Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance.
Approach:	They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.
Outcome:	The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge.

Copied to clipboard

Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

Copied to clipboard

Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang

IHEval: Evaluating Language Models on Following the Instruction Hierarchy (2025.naacl-long)

Challenge:	Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability.
Approach:	They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs .
Outcome:	The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks.

Copied to clipboard

Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu, Haoming Jiang, Xianfeng Tang, Yifan Gao, Zheng Li, Haodong Wang, Zhaoxuan Tan, Yichuan Li, Qingyu Yin, Bing Yin, Meng Jiang

UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)

Challenge:	Instruction-tuned language models (LMs) are increasingly deployed as interactive services across various applications.
Approach:	They propose a benchmark to evaluate models' ability to follow the instruction hierarchy by comparing their models to a set of benchmarks.
Outcome:	The proposed benchmark covers 3,538 examples across nine tasks covering cases where instructions in different priorities either align or conflict.

Copied to clipboard

Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang

Dialogue Systems & Conversational Agents

Challenge:	Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses.
Approach:	They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy.
Outcome:	The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy.

RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)

Copied to clipboard

Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Challenge:	Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation.
Approach:	They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows.
Outcome:	The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases.

AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (2023.acl-short)

Copied to clipboard

Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

Dialogue Systems & Conversational Agents

Challenge:	Existing research on information-seeking conversations is stymied by the lack of training data.
Approach:	They propose to use autoconv for synthetic conversation generation to capture the characteristics of the information-seeking process and fine tune an LLM with a few human conversations to generate synthetic conversations with high quality.
Outcome:	The proposed model improves on two commonly-used datasets and alleviates the dependence on human annotation.

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

Copied to clipboard

Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems (2023.acl-long)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

Copied to clipboard

Yajiao Liu, Xin Jiang, Yichun Yin, Yasheng Wang, Fei Mi, Qun Liu, Xiang Wan, Benyou Wang

PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)

Challenge:	Recent studies have found that Task-oriented Dialogue systems can be more suitable for human users.
Approach:	They propose a framework to optimize ToD systems by leveraging Multiple User SimulaTors.
Outcome:	The proposed framework improves performance on multiWOZ with human evaluations and automatic evaluations.

Copied to clipboard

Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin

Explainability & Interpretability in NLP

Challenge:	Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management.
Approach:	They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory.
Outcome:	The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models.

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)

Copied to clipboard

Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin

Reasoning

Challenge:	Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage.
Approach:	They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents.
Outcome:	Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget.

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)

Copied to clipboard

Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)

Challenge:	Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics.
Approach:	They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval.
Outcome:	The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power.

Copied to clipboard

Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei

Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)

Challenge:	Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation.
Approach:	They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.
Outcome:	The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination.

Copied to clipboard

Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, Qun Liu

BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search (2026.findings-acl)

Challenge:	Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes.
Approach:	They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions.
Outcome:	The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art.

Copied to clipboard

Shiyu Liu, Yongjing Yin, Jianhao Yan, Yunbo Tang, Qinggang Zhang, Bei Li, Xin Chen, Jingang Wang, Xunliang Cai, Jinsong Su

Reasoning

Challenge:	Existing RL-based agentic search models fail to recognize reasoning boundaries and rarely admit "I DON'T KNOW" lack of reliability leads to plausible but unreliable answers, introducing significant risks .
Approach:	They propose a framework to cultivate reliable boundary awareness without compromising accuracy.
Outcome:	Experiments show that the proposed framework improves the reliability of agentic search models.

TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020.emnlp-main)

Copied to clipboard

Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu

CTR-Guided Generative Query Suggestion in Conversational Search (2025.emnlp-industry)

Challenge:	Transformer-based pre-training models like BERT are computationally expensive and limited to resource-constrained devices.
Approach:	They propose a method which ternarizes the weights in a fine-tuned BERT model.
Outcome:	The proposed method outperforms the other methods on the GLUE and SQUAD benchmarks while being 14.9x smaller.

Copied to clipboard

Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin

SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2025.emnlp-main)

Challenge:	Generating effective query suggestions requires aligning model outputs with user click preferences.
Approach:	They propose a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.
Outcome:	The proposed framework outperforms strong baselines in CTR, relevance, diversity and diversity.

Copied to clipboard

Yuanyang Yin, Yaqi Zhao, Yajie Zhang, Yuanxing Zhang, Ke Lin, Jiahao Wang, Xin Tao, Pengfei Wan, Wentao Zhang, Feng Zhao

bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) integrate visual and textual inputs, yet modality alignment remains one of the most challenging aspects.
Approach:	They propose a token-level supervision alignment method that enables more precise visual-text alignment during pretraining.
Outcome:	The proposed method improves performance across various model sizes, with smaller models benefiting the most.

Copied to clipboard

Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu

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

Challenge:	Pre-training large language models can be expensive and wasteful.
Approach:	They propose a method which can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and a two-stage learning method to further accelerate the pre-training.
Outcome:	The proposed method can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model.

Copied to clipboard

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.

Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)

Copied to clipboard

Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah

Responsible & Trustworthy NLP

Challenge:	Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns.
Approach:	They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP.
Outcome:	Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines.

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

Copied to clipboard

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

TinyBERT: Distilling BERT for Natural Language Understanding (2020.findings-emnlp)

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.

Copied to clipboard

Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

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

Challenge:	Pre-trained language models are computationally expensive and difficult to efficiently execute on resource-restricted devices.
Approach:	They propose a Transformer distillation method that performs Transformer distillations at pre-training and task-specific learning stages.
Outcome:	The proposed method accelerates inference and reduces model size while maintaining accuracy.

Copied to clipboard

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

WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)

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.

Copied to clipboard

Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin

CORN: Co-Reasoning Network for Commonsense Question Answering (2022.coling-1)

Challenge:	Recent work shows that Code Large Language Models can address a wide range of code-related tasks.
Approach:	They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models.
Outcome:	The proposed model outperforms open-source models in generalization ability across code-related tasks.

Copied to clipboard

Xin Guan, Biwei Cao, Qingqing Gao, Zheng Yin, Bo Liu, Jiuxin Cao

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

Challenge:	Existing work uses two independent modules to model QA content and external commonsense knowledge graph (KG) Existing research uses two separate modules to create QA contextual text representations and relationships between QA entities.
Approach:	They propose a commonsense question answering (QA) model that uses two independent modules to model QA contextual text representation and relationships between QA entities in KG.
Outcome:	The proposed model achieves state-of-the-art on QA benchmarks in the CommonsenseQA and OpenBookQA datasets.

Copied to clipboard

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.

NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)

Copied to clipboard

Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

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

Challenge:	Hot news is one of the most popular topics in daily conversations.
Approach:	They propose a task where a dialogue system can lead the conversation based on key topics of the news.
Outcome:	The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios .

Copied to clipboard

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

FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)

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.

Copied to clipboard

Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin

Commonsense Reasoning

Challenge:	Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships.
Approach:	They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph.
Outcome:	The proposed framework can model e-commerce knowledge and have many potential applications.

Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment (2025.emnlp-main)

Copied to clipboard

Tong Zhang, Kuofeng Gao, Jiawang Bai, Leo Yu Zhang, Xin Yin, Zonghui Wang, Shouling Ji, Wenzhi Chen

AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models (2021.acl-long)

Challenge:	Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are vulnerable to data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet.
Approach:	They propose an Optimal Transport-based framework to reconstruct image-caption pairs and propose an optimal transport-based distance measure to re-assign new captions based on the proposed optimal transport distance.
Outcome:	The proposed framework reduces the attack success rates of poisoning attacks to 0% in most cases.

Copied to clipboard

Yichun Yin, Cheng Chen, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu