Papers by Xin Luo

Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)

Challenge:	a growing number of cloud-based inference services are relying on SMPC to protect data privacy.
Approach:	They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance.
Outcome:	The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE .

Copied to clipboard

Xin Liu, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Min Zhang, Haiying Zhang, Jinsong Su

Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)

Challenge:	Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks .
Approach:	They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token .
Outcome:	The proposed approach allows for more efficient and better performed NLG models.

Copied to clipboard

Zheheng Luo, Xin Zhang, Xiao Liu, Haoling Li, Yeyun Gong, Qi Chen, Peng Cheng

RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)

Challenge:	Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining.
Approach:	They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones.
Outcome:	The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks.

Copied to clipboard

Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)

Challenge:	Xia et al., 2018) demonstrate that a large language model can generate and maintain high-quality code documentation.
Approach:	They propose a large language model powered open-source framework for generating, maintaining, and updating code documentation.
Outcome:	The proposed framework generates high-quality documentation for the entire project.

Copied to clipboard

Di Luo, Weiheng Liao, Shuqi Li, Xin Cheng, Rui Yan

Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance.
Approach:	They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks.
Outcome:	The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets.

Copied to clipboard

Chaofan Tao, Lu Hou, Haoli Bai, Jiansheng Wei, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

Challenge:	Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications.
Approach:	They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators.
Outcome:	The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction.

Copied to clipboard

Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

Can Language Models Capture Human Writing Preferences for Domain-Specific Text Summarization? (2025.findings-acl)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

Copied to clipboard

Jingbao Luo, Ming Liu, Ran Liu, Yongpan Sheng, Xin Hu, Gang Li, WupengNjust WupengNjust

Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)

Challenge:	Recent studies employ large language models as auxiliary tools for humancentered NLP.
Approach:	They construct a model to capture human writing preferences by fine-tuning pre-trained models with data and designing prompts to optimize the output of large language models.
Outcome:	The proposed model captures human writing preferences through the dimensions of length, content depth, tone & style, and summary format.

Copied to clipboard

Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

Adaptive Nearest Neighbor Machine Translation (2021.acl-short)

Challenge:	Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory.
Approach:	They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules.
Outcome:	The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin.

Copied to clipboard

Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

Machine Translation

Challenge:	kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Approach:	They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Outcome:	The proposed method outperforms the existing model on four benchmark datasets and is open-source.

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Copied to clipboard

Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Multilinguality

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

MAGRET: Machine-generated Text Detection with Rewritten Texts (2025.coling-main)

Copied to clipboard

Yifei Huang, Jiuxin Cao, Hanyu Luo, Xin Guan, Bo Liu

Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation (N18-1)

Challenge:	Existing studies focus on detecting machine-generated text in open-source models, but their performance on closed-source large models is limited.
Approach:	They propose a method to detect rewritten text from large language models using a BERT encoder and propose to refine it to achieve semantic alignment.
Outcome:	The proposed method outperforms baseline methods on three text-generated datasets.

Copied to clipboard

Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo

Information Extraction & Text Mining

Challenge:	Existing models for user reviews are limited by data sparsity and lack of data.
Approach:	They propose to integrate LSTM and Topic Modeling to extract review information for recommender systems by utilizing user reviews.
Outcome:	The proposed model outperforms existing models on Amazon review dataset and shows better ability on making topic clustering than traditional topic model based method.

Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)

Copied to clipboard

Xueqiao Zhang, Chao Zhang, Jingtao Xu, Yifan Zhu, Xin Shi, Yi Yang, Yawei Luo

MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (2025.findings-emnlp)

Challenge:	Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans.
Approach:	They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles.
Outcome:	The proposed framework combines adaptive temporal sampling with dynamic and static role profiles.

Copied to clipboard

Yifan Zhu, Chao Zhang, Xin Shi, Xueqiao Zhang, Yi Yang, Yawei Luo

Robustness in NLP

Challenge:	Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains .
Approach:	They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks .
Outcome:	The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm .

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)

Copied to clipboard

Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

Challenge:	Existing solutions for visual document understanding lack granularity of document textlines.
Approach:	They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts.
Outcome:	The proposed system performs better on various VDU tasks in English and Chinese.

Copied to clipboard

Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

Copied to clipboard

Wenyang Luo, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Reasoning

Challenge:	a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages.
Approach:	They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance .
Outcome:	The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages.

Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)

Copied to clipboard

Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing

Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking (2022.findings-acl)

Challenge:	Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters.
Approach:	They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers.
Outcome:	The proposed method improves performance across sequence- and token-level lowresource tasks.

Copied to clipboard

Tianyi Luo, Rui Meng, Xin Wang, Yang Liu

URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)

Challenge:	Existing methods for predicting research replication are insufficient especially for long research papers.
Approach:	They propose to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to leverage large corpus of unlabeled datasets.
Outcome:	The proposed model can provide sentence-level explanations and leverage large unlabeled datasets to boost interpretability and improve prediction performance.

Copied to clipboard

Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, Yue Yu

Beyond Pedagogical Principles: Multi-Horizon Preference Optimization for Efficient Socratic Tutoring (2026.acl-long)

Challenge:	Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation.
Approach:	They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks.

Copied to clipboard

Xin Shi, Chao Zhang, Yifan Zhu, Xueqiao Zhang, Yawei Luo

Green & Sustainable NLP

Challenge:	Existing methods for developing LLMs are constrained by static data or sparse reward signals in online settings.
Approach:	They propose a framework that iteratively refines tutor agents using a multi-horizon reward function within a dynamic teacher-student simulation environment.
Outcome:	The proposed framework improves model performance and balances principles and effectiveness compared to baselines.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

Copied to clipboard

Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

Copied to clipboard

Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench.
Approach:	They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development.
Outcome:	The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development.

AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)

Copied to clipboard

Ziyang Luo, Xin Li, Hongzhan Lin, Jing Ma, Lidong Bing

Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)

Challenge:	proprietary large language models (LLMs) have demonstrated impressive code generation performance.
Approach:	They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution.
Outcome:	The proposed framework outperforms baseline model and code generation methods on three popular benchmarks.

Copied to clipboard

Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu

Challenge:	Positional biases in large language models hinder their ability to process long inputs.
Approach:	They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information.
Outcome:	The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks.

TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)

Copied to clipboard

Bo Lv, Xin Liu, Kaiwen Wei, Ping Luo, Yue Yu

To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending (2026.findings-acl)

Challenge:	Large language models (LLMs) have produced impressive results in the field of Multilingual Neural Machine Translation (MNMT).
Approach:	They propose a Teacher Assistant enhanced Knowledge Distillation method to augment knowledge transfer capacity from closed-source MNMT models.
Outcome:	The proposed method outperforms the state-of-the-art KD methods on both WMT22 and FLORES-101 test sets.

Copied to clipboard

Jin Gan, Xin Li, Jun Luo

Challenge:	Existing approaches to inference-time alignment are expensive and only offer guidances during output generation.
Approach:	They propose an inference-time alignment framework that shifts from binary decisions to creating hybrid distributions integrating both models’ knowledge.
Outcome:	The proposed framework reduces the number of inference-time alignment interventions and improves performance on challenging model pairs.

Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)

Copied to clipboard

Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li

Programming Languages in NLP

Challenge:	Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs.
Approach:	They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs.
Outcome:	Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks.

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks (2026.acl-long)

Copied to clipboard

Zhihao Luo, Wentao Yan, Jingyu Gong, Min Wang, Zhizhong Zhang, Xuhong Wang, Yuan Xie, Xin Tan

Challenge:	Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms.
Approach:	They propose a visual-target trajectory collection pipeline that generates trajectories for GUI and embodied tasks using a single formulation.
Outcome:	The proposed agent outperforms state-of-the-art agents in GUI navigation, spatial affordance prediction, and embodied navigation.

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Copied to clipboard

Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

Copied to clipboard

Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley

Reasoning

Challenge:	Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning.
Approach:	They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence.
Outcome:	The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence.

Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (N18-1)

Copied to clipboard

Hai Ye, Xin Jiang, Zhunchen Luo, Wenhan Chao

Explainability & Interpretability in NLP

Challenge:	Existing work on court view generation from fact descriptions has improved the working efficiency of legal assistant systems.
Approach:	They propose to decode court views conditioned on encoded charge labels from the fact description in a criminal case to improve interpretability of charge prediction systems.
Outcome:	The proposed model can generate court views conditioned on encoded charge labels.

Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)

Copied to clipboard

Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen

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

Challenge:	Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent.
Approach:	They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons.
Outcome:	The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons.

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.

DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)

Copied to clipboard

Chao Zhang, Xin Shi, Xueqiao Zhang, Yifan Zhu, Yi Yang, Yawei Luo

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

Challenge:	Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs.
Approach:	They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation.
Outcome:	The proposed framework outperforms baselines, reducing preference bias and improving response quality.

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

Interpretable Rationale Augmented Charge Prediction System (C18-2)

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

Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, Wenjia Ma

Explainability & Interpretability in NLP

Challenge:	Existing studies treat charge prediction as a text classification problem, but in the field of justice, every decision may be a matter of life and death.
Approach:	They propose to extract readable rationales from text and then create a rationale augmented classification model to enhance the prediction accuracy.
Outcome:	The proposed system can extract readable rationales in a high consistency with manual annotation and is comparable with the attention model in prediction accuracy.

LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)

Copied to clipboard

Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen

CAME: Confidence-guided Adaptive Memory Efficient Optimization (2023.acl-long)

Challenge:	a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented.
Approach:	They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs).
Outcome:	The proposed library is based on extensive experiments in a variety of evaluation settings.

Copied to clipboard

Yang Luo, Xiaozhe Ren, Zangwei Zheng, Zhuo Jiang, Xin Jiang, Yang You

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

Challenge:	Existing memory-efficient methods require second-moment estimates of the per-parameter gradients to maintain their performance.
Approach:	They propose to use memory-efficient optimizers to reduce memory usage by preserving second-moment estimates of gradients.
Outcome:	The proposed method achieves fast convergence and lower memory usage across training tasks.

Copied to clipboard

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

Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.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

Yu Fu, Chen Luo, Josef Valvoda, Xin Zhang, Xuejing Lei, Xiao Pan, Hui Liu, Yue Dong

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored.
Approach:	They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context.
Outcome:	The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency.

Copied to clipboard

Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

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

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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.

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

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

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.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.

Copied to clipboard

Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025.findings-acl)

Challenge:	Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach:	They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome:	The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.

Copied to clipboard

Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)

Challenge:	Existing methods for active retrieval (AR) rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries.
Approach:	They propose a method to identify knowledge boundaries in active retrieval by retrieving historical queries as high-confidence in-context examples.
Outcome:	Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training.

Copied to clipboard

Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria

CRST: a Claim Retrieval System in Twitter (C18-2)

Challenge:	Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs).
Approach:	They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions .
Outcome:	The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals.

Copied to clipboard

Wenjia Ma, WenHan Chao, Zhunchen Luo, Xin Jiang

Information Retrieval

Challenge:	CRST retrieves tweets containing arguments for controversial topics from Twitter.
Approach:	They propose a system that retrieves tweets containing claims for a given topic from Twitter.
Outcome:	The proposed system outperforms existing claims retrieval and argument mining systems.

Process-based Self-Rewarding Language Models (2025.findings-acl)

Copied to clipboard

Shimao Zhang, Xiao Liu, Xin Zhang, Junxiao Liu, Zheheng Luo, Shujian Huang, Yeyun Gong

Reasoning

Challenge:	Existing methods to reward LLMs' outputs are not effective in mathematical reasoning scenarios and may lead to a decline in performance.
Approach:	They propose a process-based self-rewarding pipeline that integrates long-thought reasoning, step-wise LLM-as-a-Judge, and step- wise preference optimization within the existing paradigm.
Outcome:	The proposed model improves the performance of Large Language Models on multiple mathematical reasoning benchmarks and shows that it can surpass human capabilities.

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Copied to clipboard

Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen