Papers by Zhiyuan Liu
Recyclable Tuning for Continual Pre-training (2023.findings-acl)
Copied to clipboard
Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded. |
| Approach: | They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance . |
| Outcome: | The proposed method improves the convergence and performance of the upgraded PLM. |
FLIQA-AD: a Fusion Model with Large Language Model for Better Diagnose and MMSE Prediction of Alzheimer’s Disease (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing classification and regression models that only extract finer-grained information from magnetic resonance imaging (MRI) may not be effective for Alzheimer's disease (AD). |
| Approach: | They propose to use a 3D Adapter in a Vision Transformer to extract the patient's EHR information and questions related to the disease as text prompts. |
| Outcome: | The proposed model can discriminate and predict the corresponding MMSE score based on the extracted brain structural information and textual content . |
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited. |
| Approach: | a new benchmark is designed to diagnose reliability in text-guided medical image editing. a clinically grounded evaluation framework measures Editing Accuracy, Context Preservation, and Visual Quality. |
| Outcome: | a new benchmark is designed to diagnose reliability in medical image editing. |
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
Copied to clipboard
Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter (2023.emnlp-main)
Copied to clipboard
| Challenge: | Language Models (LMs) have demonstrated impressive molecule understanding ability on 1D text-related tasks, but lack 2D graph perception, a critical ability of human professionals in comprehending molecules’ topological structures. |
| Approach: | They propose to combine a cross-modal projector and a uni-modal adapter to enable an LM to understand both text- and graph-based molecular contents via a Q-Former. |
| Outcome: | The proposed model outperforms the baselines on tasks such as molecule captioning, IUPAC name prediction, and molecule-text retrieval. |
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)
Copied to clipboard
| Challenge: | Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models. |
| Approach: | They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates. |
| Outcome: | The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates. |
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies suggest that pre-trained language models have gained rich knowledge during pre-training. |
| Approach: | They propose to tune pre-trained language models with task-specific prompts to improve and stabilize prompttuning. |
| Outcome: | Extensive experiments on zero and few-shot text classification tasks show that prompt-tuning improves and stabilizes prompttun-ing. |
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
Copied to clipboard
| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
Sparse Low-rank Adaptation of Pre-trained Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained large language models in a parameter-efficient manner are gaining traction within the research community. |
| Approach: | They propose a method of low-rank adaptation that enables dynamic adjustments to the intrinsic rank during the adaptation process. |
| Outcome: | The proposed approach outperforms the current method with a fixed and unalterable intrinsic rank and a low-rank adaptation process. |
Denoising Distantly Supervised Open-Domain Question Answering (P18-1)
Copied to clipboard
| Challenge: | Existing DS-QA models ignore rich information contained in other paragraphs and are noisy . Existing systems rely on pre-identified relevant texts, which do not always exist in real-world QA scenarios. |
| Approach: | They propose a model which uses a paragraph selector to filter out noisy paragraphs and a reader to extract the correct answer from denoised paragraphs. |
| Outcome: | The proposed model can capture useful information from noisy data and achieve significant improvements on open domain question answering. |
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)
Copied to clipboard
Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji
| Challenge: | Existing models of robustness evaluation are incomprehensive, impractical, and invalid . |
| Approach: | They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks. |
| Outcome: | The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol. |
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies. |
| Approach: | They propose an automated repair approach to address catastrophic-neglect in T2I DMs. |
| Outcome: | The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines. |
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)
Copied to clipboard
Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy. |
| Approach: | They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses . |
| Outcome: | The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. |
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
Copied to clipboard
| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators (2025.findings-acl)
Copied to clipboard
Jianling Li, ShangZhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, WangHaojie WangHaojie, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Triton is a high-level Python-like programming language for building efficient GPU kernels. |
| Approach: | They propose a TritonBench benchmark that provides a comprehensive evaluation of Tritonic operators on widely deployed GPUs. |
| Outcome: | The proposed benchmarks show that current LLMs struggle to generate efficient Triton operators on widely deployed GPUs aligned with industry applications. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
Copied to clipboard
Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)
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
| 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. |
Fine-grained Fact Verification with Kernel Graph Attention Network (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for fact verification are based on dot-product attentions, but kernel-based attentions focus more on relevant evidence sentences and meaningful clues in the evidence graph. |
| Approach: | They propose a kernel-based attention network which conducts more fine-grained fact verification with kernel-basic attentions. |
| Outcome: | The proposed task achieves a 70.38% FEVER score and significantly outperforms existing fact verification models on FEVER, a large-scale benchmark for fact verification. |
Coreferential Reasoning Learning for Language Representation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. |
| Approach: | They propose a language representation model that captures coreferential relations in context. |
| Outcome: | The proposed model can achieve significant improvements on downstream NLP tasks while maintaining comparable performance to baseline models on other common NLP task. |
On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)
Copied to clipboard
Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios. |
| Approach: | They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments. |
| Outcome: | The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings. |
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
Copied to clipboard
| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Long Text and Multi-Table Summarization: Dataset and Method (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing document summarization methods focus on the text and filter out the non-textual content. Existing methods cannot meet the requirements of summarizing long text and multiple tables in each report. |
| Approach: | They propose a dataset for automatic document summarization that uses text and tabular data to produce a concise summary covering the input document's salient information. |
| Outcome: | The proposed method can produce a concise summary covering the input document's salient information. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
Copied to clipboard
Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
Copied to clipboard
Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
Copied to clipboard
Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
Enhancing Agent Learning through World Dynamics Modeling (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics. |
| Approach: | They propose a framework that discovers world dynamics from a small number of demonstrations, verifies the correctness of these dynamics, and evolves new, advanced dynamics tailored to the current situation. |
| Outcome: | The proposed framework discovers, verifies, and evolves world dynamics from a small number of demonstrations, and compares the automatically generated dynamics with human-annotated world dynamics. |
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models (2022.acl-long)
Copied to clipboard
| Challenge: | Existing feature-based model selection methods are vulnerable to fine-tuning and are not reliable indicators for the PTM’s transferability. |
| Approach: | They propose feature-based model selection methods which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. |
| Outcome: | The proposed methods can make FMS mistakenly judge transferability of models and can be combined with the backdoor attack to misguide the FMS to select poisoned models. |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge. |
| Approach: | They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts. |
| Outcome: | The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts. |
ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)
Copied to clipboard
Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
| Challenge: | Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills. |
| Approach: | They propose a unified QA paradigm that solves various tasks through a single model. |
| Outcome: | The proposed model improves QA-centric ability on 11 QA benchmarks. |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
Copied to clipboard
| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training (2025.naacl-long)
Copied to clipboard
| Challenge: | Current PP methods face severe bottlenecks, including pipeline bubbles and memory footprint. |
| Approach: | They propose a sequence-level one-forward-one-backward (1F1B) PP method for training LLMs on long sequences with high throughput and memory efficiency. |
| Outcome: | The proposed method achieves 1.14X training throughput with half memory footprint compared to baseline methods . it trains an LLM with 30B parameters on sequences up to 64k tokens using 64X NVIDIA A100 GPUs . |
LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks (2024.acl-long)
Copied to clipboard
| Challenge: | LoRA-Flow uses lightweight modules to customize large language models for downstream tasks . previous work on LoRA combination relied on task-level weights for each involved LoRA . |
| Approach: | They propose a LoRA-Flow approach that uses dynamic weights to adjust the impact of different LoRAs. |
| Outcome: | The proposed method outperforms baselines with task-level weights on six generative tasks. |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
Copied to clipboard
Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
| Approach: | They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE . |
| Outcome: | The proposed methods can extract relational facts from text, but they are still lacking in the current field. |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
Copied to clipboard
Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
ProtT3: Protein-to-Text Generation for Text-based Protein Understanding (2024.acl-long)
Copied to clipboard
| Challenge: | Language Models excel in understanding textual descriptions of proteins, but struggle to process texts. |
| Approach: | They propose a framework for Protein-to-Text Generation for Text-based Protein Understanding that integrates a PLM as its protein understanding module. |
| Outcome: | The proposed framework surpasses existing baselines and is highly efficient in protein-to-text generation. |
Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing textual backdoor attacks are vulnerable to backdoors . researchers add extra training task to distinguish poisoned and clean data . |
| Approach: | They propose two tricks that make existing backdoor attacks much more harmful . first trick is to add an extra task to distinguish poisoned and clean data . second trick is using all the clean training data rather than the original clean data. |
| Outcome: | The proposed tricks can significantly improve attack performance in three tough situations including clean data fine-tuning, low-poisoning-rate, and label-consistent attacks. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
Copied to clipboard
Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
Copied to clipboard
Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022.coling-1)
Copied to clipboard
| Challenge: | Existing generative methods focus on a single task at a time. |
| Approach: | They propose a unified generative multi-task framework that can solve multiple ABSA tasks . they propose to control the type of task prompts consisting of multiple element prompts . |
| Outcome: | The proposed framework achieves state-of-the-art results in almost all ABSA tasks and competitive results in task transfer scenarios. |
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP (2022.emnlp-main)
Copied to clipboard
| Challenge: | Textual adversarial samples are often misrepresented in research on security, evaluation, explainability, and data augmentation. |
| Approach: | They propose to use adversarial samples to evaluate their methods on security tasks to demonstrate the real-world concerns rather than developing impractical methods. |
| Outcome: | The proposed method has higher practical value than the current benchmark. |
Adversarial Training for Weakly Supervised Event Detection (N19-1)
Copied to clipboard
| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models (2023.acl-demo)
Copied to clipboard
| Challenge: | Existing implementations that modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM have limited the practicality and flexibility of delta tuning. |
| Approach: | They propose an open-source library that provides a plug-and-play implementation of delta tuning methods for pre-trained models. |
| Outcome: | The proposed methods eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTM. |
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm (2022.findings-naacl)
Copied to clipboard
| Challenge: | Prompt-based learning inherits the vulnerability from pre-training, where model predictions can be misled by inserting triggers into the text. |
| Approach: | They propose a potential solution to mitigate this vulnerability by injecting triggers into pre-trained language models using only plain text. |
| Outcome: | The proposed learning paradigm inherits the vulnerability from the pre-training stage . it can totally control or severely decrease the performance of prompt-based models . |
Few-shot Classification with Hypersphere Modeling of Prototypes (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fewshot learning use embeddings in space, but they lack expressivity and are difficult to perform statistically. |
| Approach: | They propose a method where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere’s center and the radius. |
| Outcome: | The proposed method is much more expressive than embeddings and performs better than statistical modeling. |
Multimodal Causal Reasoning Benchmark: Challenging Multimodal Large Language Models to Discern Causal Links Across Modalities (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing MLLMs lack robustness in multimodal causal reasoning compared to their performance in textual settings. |
| Approach: | They propose a novel multimodal chain-of-thought (CoT) reasoning benchmark that leverages siamese images and text pairs to challenge MLLMs. |
| Outcome: | The proposed benchmark leverages siamese images and text pairs to challenge MLLMs. |
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing language representation models (PLMs) cannot capture factual knowledge from text. |
| Approach: | They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM. |
| Outcome: | The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks. |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
Exploring Lottery Prompts for Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to optimize pre-trained language models are expensive and slow to scale. |
| Approach: | They propose to search for instance-level lottery prompts and generalize them to unseen data . they validate the assumption that for every instance, there is almost always a lottery prompt that induces the correct prediction from the PLM . |
| Outcome: | The proposed method can achieve comparable results with other gradient-free and optimization-free baselines. |
PersLLM: A Personified Training Approach for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication and extensive knowledge reserves. |
| Approach: | They propose a framework for better data construction and model tuning to unlock the potential of LLM personification by using Chain-of-Thought prompting and anti-induction. |
| Outcome: | The proposed framework improves data construction and model tuning for insufficient data usage and rigid behavior patterns. |
UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)
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
| 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. |
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution. |
| Approach: | They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training. |
| Outcome: | The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange. |
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to verify scientifically false online information are limited by the lack of training data in the scientific domain. |
| Approach: | They propose an in-domain language modeling method for fact extraction and verification systems . they use SCIFACT to extract scientifically false online information . |
| Outcome: | The proposed method improves accuracy 30% on SCIFACT dataset . state-of-the-art model achieves only 46.6% precision, which is hard to be trusted for users. |
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing open-source MLLMs fail to fully capture dense information embedded in charts . current models still face significant challenges in understanding and analyzing visual tasks such as captioning and question answering. |
| Approach: | They propose a chart-to-code MLLM which leverages Code LLMs as the language backbone to enhance the executability of the generated code. |
| Outcome: | The proposed model surpasses existing open-source models on chart-to-code benchmarks with only 7B parameters and provides lossless representations that contain all critical details. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
Copied to clipboard
Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Few-NERD: A Few-shot Named Entity Recognition Dataset (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to few-shot named entity recognition (NER) focus on coarse-grained entities with few examples, while most unseen entities are fine-grounded. |
| Approach: | They present a human-annotated few-shot named entity recognition dataset . they construct benchmark tasks to assess the generalization capability of models . |
| Outcome: | The proposed model is the first few-shot NER dataset and the largest human-crafted NER data set. |
KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)
Copied to clipboard
Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals. |
| Approach: | They propose a self-supervised framework that enhances RAG systems through efficient model adaptation. |
| Outcome: | The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models. |
Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models (2025.findings-acl)
Copied to clipboard
Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, Ge Yu
| Challenge: | Existing evaluation metrics cannot fairly evaluate the outputs of RAG models during training and evaluation. |
| Approach: | They propose a method which prompts LLMs to generate different judgments based on various combinations of judgment dimensions and utilizes the judge-consistency to evaluate these judgments. |
| Outcome: | The proposed method generates more accurate evaluations for RAG models across different RAG model and datasets. |
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment (2024.emnlp-main)
Copied to clipboard
Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Zexu Sun, Bowen Sun, Huimin Chen, Ruobing Xie, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing algorithms for achieving optimal alignment are mostly unidirectional . a recent study suggests that large language models can be ground with evident preferences . |
| Approach: | They propose to ground large language models with evident preferences . they propose to use controllable preference optimization to specify different objectives . |
| Outcome: | The proposed models can provide responses that match various preferences among the ”3H” desiderata. |
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs (2025.acl-long)
Copied to clipboard
Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Sun Ao, Hao Zhou, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Long-context inference is crucial for advancing large language models, but its prefill speed remains a bottleneck. |
| Approach: | They propose an efficient long-context inference framework that leverages multi-host approximate attention to enhance prefill speed. |
| Outcome: | The proposed framework achieves speedups of 9.2, 4.2, and 1.6 without any degradation in performance. |
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that neural natural language processing models are vulnerable to backdoor attacks. |
| Approach: | They propose to inject neural models with backdoors activated by word substitution . their results raise a serious alarm to the security of NLP models, they argue . |
| Outcome: | The proposed backdoors are activated by a learnable combination of word substitution and exhibit higher invisibility than previous methods. |
ReactXT: Understanding Molecular “Reaction-ship” via Reaction-Contextualized Molecule-Text Pretraining (2024.findings-acl)
Copied to clipboard
Zhiyuan Liu, Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, Tat-Seng Chua
| Challenge: | Molecular-text modeling is an emerging research field that aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge. |
| Approach: | They propose a new method for reaction-text modeling that uses three types of input contexts to incrementally pretrain LMs. |
| Outcome: | The proposed method improves experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis. |
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games (2024.findings-acl)
Copied to clipboard
| Challenge: | In this study, we explore the application of Large Language Models (LLMs) in Jubensha, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. |
| Approach: | They propose to use large language models to foster AI agent development in Jubensha, a Chinese detective role-playing game. |
| Outcome: | The proposed framework enables AI agents to engage in Jubensha games autonomously. |
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
Copied to clipboard
Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning (2026.findings-acl)
Copied to clipboard
Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Qixia Jiang
| Challenge: | Large language models demonstrate remarkable zero-shot generalization, but adapting to downstream tasks requires continual fine-tuning. |
| Approach: | They propose a method that incrementally constructs a pool of frozen, task-specific LoRA experts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in task-free and blurred-boundary settings. |
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
Copied to clipboard
Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
| Approach: | They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem. |
| Outcome: | The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings. |
Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms. |
| Approach: | They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately. |
| Outcome: | The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods. |
Neural Gibbs Sampling for Joint Event Argument Extraction (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods for event argument extraction cannot adequately model the correlation between event arguments and their roles. |
| Approach: | They propose a Bayesian model to jointly extract event arguments using Gibbs sampling . they train two neural networks to model prior distribution and conditional distribution over event arguments . |
| Outcome: | The proposed model can achieve comparable results to existing methods on two widely-used datasets. |
Sub-Character Tokenization for Chinese Pretrained Language Models (2023.tacl-1)
Copied to clipboard
Chenglei Si, Zhengyan Zhang, Yingfa Chen, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun
| Challenge: | Existing tokenization methods for Chinese PLMs treat each character as an indivisible token, but ignore the unique feature of the writing system where additional linguistic information exists below the character level. |
| Approach: | They propose to encode Chinese characters into short sequences and construct Chinese vocabulary based on the encoded text. |
| Outcome: | The proposed tokenizers can tokenize inputs into much shorter sequences, improving computational efficiency. |
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks. |
| Approach: | They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework. |
| Outcome: | The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks. |
Automatically Select Emotion for Response via Personality-affected Emotion Transition (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on rendering specified emotions in responses, yet the individual difference in emotion expression is overlooked. |
| Approach: | They propose to equip a dialog system with personality and enable it to select emotions in responses like humans. |
| Outcome: | The proposed system can select emotions in responses like humans by simulating the emotion transition of humans in conversation. |
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)
Copied to clipboard
Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models. |
| Approach: | They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots. |
| Outcome: | Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines. |
ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information. |
| Approach: | They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. |
| Outcome: | The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings. |
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)
Copied to clipboard
Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined. |
| Approach: | They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process. |
| Outcome: | The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness. |
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)
Copied to clipboard
| Challenge: | OpenNRE provides a framework to implement neural relation extraction (RE) . the toolkit provides various functional modules based on TensorFlow and PyTorch . |
| Approach: | OpenNRE is an open-source framework to implement neural relation extraction models. they also release an online system to meet real-time extraction without any training and deployment. |
| Outcome: | OpenNRE provides a framework to implement neural models for relation extraction (RE) the toolkit also includes an online system to meet real-time extraction without training and deployment . |
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for tuning pre-trained language models ignore the running cost and only optimize the terminal cost. |
| Approach: | They propose to use stochastic bridges to regularize intermediate states and use regularization as running cost of PETs. |
| Outcome: | The proposed methods can be used to tune large pre-trained language models . they can be compared to full-parameter fine-tuning by tuning a small number of parameters . |
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises. |
| Approach: | They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications. |
| Outcome: | The proposed benchmarks are based on four different tasks and are designed to maximize diversity. |
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
| Outcome: | The proposed method minimizes intra-class variance by pulling together in-domain intents belonging to the same class and maximizes inter-class variation by pushing apart samples from different classes. |
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates. |
| Approach: | They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion. |
| Outcome: | The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings. |
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing prompting methods oversimplify time-series forecasting (TSF) time-Series data are ubiquitous across various domains, including public health, finance and energy. |
| Approach: | They propose a method for prompting off-the-shelf Large Language Models (LLMs) they decompose TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each . |
| Outcome: | The proposed approach decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. |
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness. |
| Approach: | They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks. |
| Outcome: | The proposed method can handle all the textual backdoor attack situations. |
Enhancing Advanced Visual Reasoning Ability of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability. |
| Approach: | They propose a novel multi-modal in-context learning methodology to enhance LLMs’ contextual understanding and reasoning. |
| Outcome: | The proposed model achieves SOTA performance among all visual reasoning tasks and achieves a 'higher level of accuracy' than previous models. |
Fusing Highly Specialized Language Models for Comprehensive Expertise (2025.acl-long)
Copied to clipboard
Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Kaiyan Zhang, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing models that focus on language, programming code, and mathematical symbols are not able to achieve mastery of all three domains simultaneously. |
| Approach: | They propose to fuse highly-specialized models that are already sufficiently trained on different domains to achieve a highly-specific model. |
| Outcome: | The proposed model could achieve mastery of the three crucial domains simultaneously. |
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent research shows strong empirical advantages of dense retrieval in various information access scenarios, such as openQA. |
| Approach: | They propose a method which accumulates momentum negatives from past iterations and approximates future iteration with lookahead negatives as "teleportations" on web search and OpenQA, ANCE-Tele outperforms previous state-of-the-art systems of similar size and eliminates the dependency on sparse retrieval negatives. |
| Outcome: | The proposed method outperforms previous state-of-the-art systems on web search and OpenQA and is competitive among systems with significantly more parameters. |
CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
| Approach: | They propose to extend the counterfactual reasoning from the information entropy perspective to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. |
| Outcome: | The proposed method can reduce question-related bias in the auto-regressive generation task by using counterfactual entropy as an external loss. |
Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding (2024.emnlp-main)
Copied to clipboard
Weilin Zhao, Yuxiang Huang, Xu Han, Wang Xu, Chaojun Xiao, Xinrong Zhang, Yewei Fang, Kaihuo Zhang, Zhiyuan Liu, Maosong Sun
| Challenge: | Speculative decoding is a widely used method that accelerates the generation process of large language models (LLMs) drafting efficiency has become a bottleneck in the final speedup of speculative drafting, therefore generating longer drafts at less cost can lead to better speedup. |
| Approach: | They propose a method that uses existing model to drafting and target LLM to verify draft in a low-cost parallel manner. |
| Outcome: | The proposed method can achieve speedups of up to 2.4 over speculative decoding and 3.9 over vanilla decoding without fine-tuning draft and target models. |
Emergent Modularity in Pre-trained Transformers (2023.findings-acl)
Copied to clipboard
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou
| Challenge: | Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions. |
| Approach: | They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity . |
| Outcome: | The proposed structure stabilizes at the early stage, which is faster than neuron stabilization. |
Going “Deeper”: Structured Sememe Prediction via Transformer with Tree Attention (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies ignore hierarchical structures of sememes in sememe-based semantic description systems. |
| Approach: | They propose a structured sememe prediction problem to predict a sememes tree with hierarchical structures rather than a set of sememas. |
| Outcome: | The proposed model outperforms baseline models and shows its effectiveness . it predicts a sememe tree with hierarchical structures rather than a set of sememes . |
Low-Resource Name Tagging Learned with Weakly Labeled Data (D19-1)
Copied to clipboard
| Challenge: | Existing methods for name tagging in low-resource languages or domains require extensive human efforts for training annotations. |
| Approach: | They propose a neural model for name tagging based on weakly labeled (WL) data. |
| Outcome: | The proposed model outperforms existing models in five low-resource languages and fine-grained food domains and shows that it is more efficient and efficient than existing models. |
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs. |
| Approach: | They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
| Outcome: | The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
Decoder Tuning: Efficient Language Understanding as Decoding (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to adapt pre-trained models with parameters frozen are based on input-side adaptation, which requires thousands of API queries. |
| Approach: | They propose to train a model-as-a-service (MaaS) setting to provide only the inference APIs for users . they argue that input-side adaptation could be arduous due to the lack of gradient signals . |
| Outcome: | The proposed model outperforms state-of-the-art algorithms with a 200x speed-up. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
Copied to clipboard
| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen (2020.acl-main)
Copied to clipboard
| Challenge: | a curse of knowledge can impede communication between experts and laymen. |
| Approach: | They propose a task of expertise style transfer to automate the communication between experts and laymen using an annotated dataset. |
| Outcome: | The proposed task simplifies professional language and improves accuracy and expertise level of laymen descriptions using simple words. |
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
Automatic Construction of Sememe Knowledge Bases via Dictionaries (2021.findings-acl)
Copied to clipboard
| Challenge: | Sememe knowledge bases (SKBs) are used to analyze natural language processing. |
| Approach: | They propose a method to build sememe knowledge bases from an existing dictionary . they propose to use existing dictionaries to build an English and a French SKB . |
| Outcome: | The proposed method is superior to HowNet, the most widely used SKB that takes decades to build manually. |
Adversarial Multi-lingual Neural Relation Extraction (C18-1)
Copied to clipboard
| Challenge: | Existing models cannot capture consistency and diversity of relation patterns in different languages. |
| Approach: | They propose an adversarial multi-lingual neural relation extraction model which considers consistency and diversity among languages. |
| Outcome: | The proposed model outperforms the state-of-the-art models on real-world datasets. |
DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
Copied to clipboard
Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
Copied to clipboard
Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)
Copied to clipboard
| Challenge: | Existing methods of lexical sememe prediction rely on external context information of words to represent meaning. |
| Approach: | They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words. |
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)
Copied to clipboard
Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters. |
| Approach: | They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters. |
| Outcome: | The proposed method is compatible with a tunable module and tested on 11 NLP tasks. |
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data (2023.findings-acl)
Copied to clipboard
| Challenge: | Structure Aware Dense Retrieval (SANTA) model encodes user queries and structured data in one universal embedding space for retrieving structured data. |
| Approach: | They propose to use structured data and unstructured data to encode queries and structured data in one universal embedding space for retrieving structured data. |
| Outcome: | The proposed model achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting. |
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair (2024.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that INTERVENOR surpasses baseline models, exhibiting improvements of approximately 18% and 4.3% over GPT-3.5 in code generation and code translation tasks. |
| Approach: | They propose a system that prompts Large Language Models to play distinct roles during the code repair process, functioning as both a Code Learner and a code teacher. |
| Outcome: | The proposed system surpasses baseline models in code generation and code translation tasks and improves on syntax errors and assertion errors. |
BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)
Copied to clipboard
Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun
| Challenge: | Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks. |
| Approach: | They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost. |
| Outcome: | The proposed toolkit can support big model inference and tuning at extremely low computation cost. |
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)
Copied to clipboard
Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
PPT: Pre-trained Prompt Tuning for Few-shot Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored . |
| Approach: | They propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. |
| Outcome: | The proposed framework outperforms full-model tuning under full-data and few-shot learning settings. |
APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)
Copied to clipboard
Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu
| Challenge: | Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos . |
| Approach: | They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs. |
| Outcome: | The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss. |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
Copied to clipboard
| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)
Copied to clipboard
Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Qizhe Shieh, Wenmeng Zhou
| Challenge: | Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories. |
| Approach: | They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories. |
| Outcome: | The proposed system integrates LLM agents with graph database interfaces extracted from code repositories. |
Craw4LLM: Efficient Web Crawling for LLM Pretraining (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work discards over 90% of the raw data collected from web crawls, highlighting the inefficiency of current web crawlers in collecting LLM pretraining data. |
| Approach: | They propose a web crawling method that leverages the preference of LLMs as the priority score of the web crawler’s scheduler to obtain high-quality pretraining data. |
| Outcome: | The proposed method achieves high-quality pretraining data on a web graph containing 900 million webpages from a commercial search engine's index with just 21% URLs crawled. |
Cross-lingual Lexical Sememe Prediction (D18-1)
Copied to clipboard
| Challenge: | Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems . |
| Approach: | They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. |
| Outcome: | The proposed model improves on baseline methods on real-world datasets. |
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages. |
| Approach: | They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. |
| Outcome: | The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data. |
Open Hierarchical Relation Extraction (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. |
| Approach: | They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
Hao Chen, Yukun Yan, Sen Mei, Wanxiang Che, Zhenghao Liu, Qi Shi, Xinze Li, Yuchun Fan, Pengcheng Huang, Qiushi Xiong, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing RAG systems often underutilize the retrieved documents, authors say . they fail to extract and integrate key clues needed to support faithful and interpretable reasoning . |
| Approach: | a new framework extracts key clues from retrieved content and generates multiple reasoning paths . the framework optimizes the model by selecting the most appropriate reasoning path . |
| Outcome: | Experiments show that ClueAnchor outperforms baseline RAG frameworks in completeness and robustness. |
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)
Copied to clipboard
Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu
| Challenge: | Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models. |
| Approach: | They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. |
| Outcome: | Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. |
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction (2022.emnlp-main)
Copied to clipboard
Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou
| Challenge: | Existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. |
| Approach: | They construct a large-scale human-annotated ERE dataset with improved annotation schemes to address these drawbacks. |
| Outcome: | The proposed dataset is larger than existing datasets of all the ERE tasks by at least an order of magnitude. |
Train No Evil: Selective Masking for Task-Guided Pre-Training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models can't capture domain-specific and task-specific patterns because of the task-agnostic pre-training stage. |
| Approach: | They propose a task-guided pre-training stage with selective masking between general pre-train and fine-tuning to learn domain-specific patterns. |
| Outcome: | The proposed method can achieve comparable or even better performance with less than 50% of computation cost. |
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)
Copied to clipboard
Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong
| Challenge: | Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. |
| Approach: | They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process. |
| Outcome: | The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks. |
Memorize Step by Step: Efficient Long-Context Prefilling with Incremental Memory and Decremental Chunk (2024.emnlp-main)
Copied to clipboard
Zhiyuan Zeng, Qipeng Guo, Xiaoran Liu, Zhangyue Yin, Wentao Shu, Mianqiu Huang, Bo Wang, Yunhua Zhou, Linlin Li, Qun Liu, Xipeng Qiu
| Challenge: | Existing methods to optimize LLM for long sequences for long documents are slow and consume memory. |
| Approach: | They propose a method that starts with a small memory size and gradually increases it . they propose Decremental Chunk based on Incremental Memory (IMDC) which reduces chunk size while increasing memory size . |
| Outcome: | The proposed method is faster (1.45x) and reduces GPU memory consumption by 23.3% compared to fixed-size memory. |
AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage (2026.acl-long)
Copied to clipboard
Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Efficient reproduction of research papers requires deep domain expertise. |
| Approach: | They propose a framework that systematically mines implicit knowledge from the cited literature to reproduce experimental code in a complete, end-to-end manner. |
| Outcome: | The proposed framework surpasses baselines across all metrics and reproduces experimental code in a complete, end-to-end manner. |
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)
Copied to clipboard
| Challenge: | Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. |
| Approach: | They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input. |
| Outcome: | The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance. |
OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)
Copied to clipboard
Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun
| Challenge: | Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models. |
| Approach: | They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit. |
| Outcome: | The proposed toolkit supports all attack types, multilinguality, and parallel processing. |
Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)
Copied to clipboard
Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun
| Challenge: | Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context. |
| Approach: | They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining. |
| Outcome: | The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions. |
Multi-Channel Graph Neural Network for Entity Alignment (P19-1)
Copied to clipboard
| Challenge: | Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments. |
| Approach: | They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels. |
| Outcome: | The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. |
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)
Copied to clipboard
| Challenge: | Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive. |
| Approach: | They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data. |
| Outcome: | Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods. |
A Close Look into the Calibration of Pre-trained Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. |
| Approach: | They conduct fine-grained control experiments to study the dynamic change in PLMs’ calibration performance in training. |
| Outcome: | The proposed methods significantly reduce PLMs’ confidence in wrong predictions. |
Parameter-efficient Weight Ensembling Facilitates Task-level Knowledge Transfer (2023.acl-short)
Copied to clipboard
| Challenge: | Recent studies show that large pre-trained language models can be adapted to particular tasks in a parameter-efficient manner. |
| Approach: | They propose to use lightweight parameters to transfer them between tasks to obtain similarity between tasks. |
| Outcome: | The proposed methods show an improvement of 5%8% over baselines and could largely facilitate task-level knowledge transfer. |
AgentRM: Enhancing Agent Generalization with Reward Modeling (2025.acl-long)
Copied to clipboard
Yu Xia, Jingru Fan, Weize Chen, Siyu Yan, Xin Cong, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing LLM-based agents have strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. |
| Approach: | They propose a reward-based generalizable reward model to guide the policy model for effective test-time search. |
| Outcome: | The proposed agentRM outperforms existing agents on held-in tasks by 8.8 points on average. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
Copied to clipboard
Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning. |
| Approach: | They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities. |
| Outcome: | Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks. |
LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)
Copied to clipboard
Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
| Challenge: | E-commerce search relevance is a critical component of retrieval systems. |
| Approach: | They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies. |
| Outcome: | The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain. |
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)
Copied to clipboard
Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett
| Challenge: | Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available. |
| Approach: | They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. |
| Outcome: | The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains. |
Fusion-in-T5: Unifying Variant Signals for Simple and Effective Document Ranking with Attention Fusion (2024.lrec-main)
Copied to clipboard
| Challenge: | Current document ranking pipelines involve multiple ranking layers to integrate different information step-by-step. |
| Approach: | They propose a novel re-ranker Fusion-in-T5 which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. |
| Outcome: | The proposed model significantly improves ranking performance over complex cascade pipelines. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)
Copied to clipboard
| Challenge: | Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. |
| Approach: | They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently. |
| Outcome: | The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
Copied to clipboard
Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)
Copied to clipboard
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases. |
| Approach: | They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. |
| Outcome: | The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen. |
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. |
| Approach: | They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes . |
| Outcome: | The proposed model outperforms previous methods in terms of MAP and F1 scores. |
FPT: Improving Prompt Tuning Efficiency via Progressive Training (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). |
| Approach: | They propose a prompt tuning algorithm that uses a small-scale partial PLM and progressively expands its depth and width until the full-model size. |
| Outcome: | The proposed method could save over 30% of training computations while achieving comparable performance. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
Copied to clipboard
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
| 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. |
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (P18-1)
Copied to clipboard
| Challenge: | Entity-oriented search and neural-IR push the boundary of search engines from two different aspects. |
| Approach: | They propose an Entity-Duet Neural Ranking Model which integrates knowledge graphs into neural search systems. |
| Outcome: | The proposed model improves generalization ability of neural ranking models on a commercial search log. |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
Copied to clipboard
| 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. |
Legal Judgment Prediction via Topological Learning (D18-1)
Copied to clipboard
| Challenge: | Existing studies focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks. |
| Approach: | They propose a topological multi-task learning framework that incorporates multiple subtasks and DAG dependencies into judgment prediction. |
| Outcome: | The proposed model improves on baselines on all judgment prediction tasks. |
Prototypical Verbalizer for Prompt-based Few-shot Tuning (2022.acl-long)
Copied to clipboard
| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
| Approach: | They propose a prototypical verbalizer which learns prototype vectors as verbalizes by contrastive learning. |
| Outcome: | The proposed verbalizer outperforms existing verbalizing methods on topic classification and entity typing tasks. |
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models (2022.acl-short)
Copied to clipboard
| Challenge: | Existing pre-trained language models cannot recall factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. |
| Approach: | They propose to build a pluggable Entity Lookup Table (PELT) on demand by aggregating the entity’s output representations of multiple occurrences in the corpora. |
| Outcome: | The proposed model can transfer entity knowledge from out-of-domain corpora into PLMs with different architectures. |
BMCook: A Task-agnostic Compression Toolkit for Big Models (2022.emnlp-demos)
Copied to clipboard
Zhengyan Zhang, Baitao Gong, Yingfa Chen, Xu Han, Guoyang Zeng, Weilin Zhao, Yanxu Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing efforts to compress medium-sized models for specific tasks have limited results. |
| Approach: | They propose a task-agnostic compression toolkit for big models that implements quantization, pruning, distillation and MoEfication methods. |
| Outcome: | The proposed tool improves performance on a model with 3 billion parameters by 12x . it also outperforms the original model on three typical NLP benchmarks. |
Neural Collective Entity Linking (C18-1)
Copied to clipboard
| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
| Outcome: | The proposed model improves its performance on five publicly available datasets and can be used to train on Wikipedia hyperlinks to avoid overfitting and domain bias. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
Copied to clipboard
| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments. |
| Approach: | They propose a method to enhance the inference efficiency of parameter-shared PLMs by pre-training models that can achieve even greater acceleration. |
| Outcome: | The proposed method improves inference efficiency on autoregressive and autoencoding models. |
NumNet: Machine Reading Comprehension with Numerical Reasoning (D19-1)
Copied to clipboard
| Challenge: | Existing numerical MRC models are weak in numerical reasoning, such as addition, subtraction, sorting and counting. |
| Approach: | They propose a numerical MRC model that integrates numerical reasoning into existing MRC models and achieves an EM-score of 64.56% on the DROP dataset. |
| Outcome: | The proposed model outperforms all existing machine reading comprehension models by considering the numerical relations among numbers on the DROP dataset. |
Finding Skill Neurons in Pre-trained Transformer-based Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models have demonstrated superior performance on various natural language processing tasks. |
| Approach: | They find that after prompt tuning, some neurons encode task-specific skills . they also show that skill neurons are most likely generated in pre-training . |
| Outcome: | The neurons are highly predictive of task labels after prompt tuning for specific tasks. |
Cost-Optimal Grouped-Query Attention for Long-Context Modeling (2025.emnlp-main)
Copied to clipboard
Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai, Xingyu Shen, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Current GQA configurations overlook how context length influences inference cost . |
| Approach: | They propose a recipe for deriving cost-optimal GQA configurations that decouple the total head size from the hidden size and allow more flexible control over attention FLOPs. |
| Outcome: | The proposed configurations reduce memory usage and FLOPs by more than 50% compared to Llama-3's GQA, with *no degradation in model capabilities*. |
Graph Neural Networks with Generated Parameters for Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing graph neural networks can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning. |
| Approach: | They propose a graph neural network with generated parameters using natural language sentences as inputs. |
| Outcome: | The proposed model can process relational reasoning on graphs and in natural language processing tasks. |
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for textual backdoor attacks insert additional contents into normal samples as triggers, causing detection and blocking of backdoors. |
| Approach: | They propose to use syntactic structure as trigger in textual backdoor attacks . they propose to achieve similar attack performance but have higher invisibility . |
| Outcome: | The proposed method achieves almost 100% success rate but has higher invisibility and stronger resistance to defenses than the insertion-based methods. |
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)
Copied to clipboard
Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi
| Challenge: | Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential. |
| Approach: | They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling. |
| Outcome: | The proposed model improves performance on four benchmarks and significantly mitigates positional bias. |
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer (2021.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer. |
| Approach: | They propose to conduct adversarial and backdoor attacks based on text style transfer . the authors propose to use text style to alter the style of a sentence . |
| Outcome: | The proposed methods show that popular models are vulnerable to both attacks based on text style transfer . the results show that the proposed methods perform better than baselines in many aspects . |
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge (P19-1)
Copied to clipboard
| Challenge: | Existing methods for Chinese relation extraction suffer from segmentation errors and ambiguity of polysemy. |
| Approach: | They propose a multi-grained lattice framework for Chinese relation extraction . they incorporate word-level information into character sequence inputs to avoid segmentation errors . |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in distinct domains. |
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for multi-hop reasoning are not able to evaluate their interpretability . a recent study found that many paths are unreasonable . |
| Approach: | They propose a framework to evaluate the interpretability of multi-hop reasoning models . they annotate all possible rules and establish a benchmark . |
| Outcome: | The proposed framework outperforms existing models in terms of performance and interpretability. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
Copied to clipboard
Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)
Copied to clipboard
Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
| Challenge: | Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior . |
| Approach: | They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection. |
| Outcome: | The proposed training strategy boosts performance on both benchmarks. |
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models (2024.findings-acl)
Copied to clipboard
Zhicheng Guo, Sijie Cheng, Hao Wang, Shihao Liang, Yujia Qin, Peng Li, Zhiyuan Liu, Maosong Sun, Yang Liu
| Challenge: | Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning. |
| Approach: | They propose a virtual API server and stable evaluation system to assess the stability of large-scale real-time APIs. |
| Outcome: | The proposed benchmarks demonstrate the stability of the proposed system and its caching system. |
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)
Copied to clipboard
Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources. |
| Approach: | They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing. |
| Outcome: | The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus. |
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work on retrieval augmentation fine-tuned the retriever and the LM, making them closely coupled. |
| Approach: | They propose a generic retrieval plug-in that can be used to fine-tune retrieval augmentation and a LM to learn a user's preferences. |
| Outcome: | The proposed retriever improves the generalization of large language models on the MMLU and PopQA datasets by learning LM’s preferences from a known source LM . |
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance . |
| Approach: | They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences . |
| Outcome: | The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
Copied to clipboard
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training (2026.acl-long)
Copied to clipboard
Yu Liang, Liangxin Liu, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Daiting Shi
| Challenge: | ConsistRM is a self-training framework that enables effective and stable GRM training without human annotations. |
| Approach: | They propose a self-training framework that enables effective and stable GRM training without human annotations. |
| Outcome: | The proposed framework outperforms vanilla Reinforcement Fine-Tuning (RFT) by 1.5% on five benchmark datasets. |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)
Copied to clipboard
Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun
| 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. |
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction (2021.findings-acl)
Copied to clipboard
Tianyu Gao, Xu Han, Yuzhuo Bai, Keyue Qiu, Zhiyu Xie, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Distantly supervised relation extraction (RE) has attracted much attention in the past few years . previous methods to evaluate models manually or directly on autolabeled data have produced inaccurate evaluations . |
| Approach: | They propose to use distant supervision to generate large-scale autolabeled data . they build manually-annotated test sets for two DS-RE datasets and evaluate models . |
| Outcome: | The proposed method produces 53% wrong labels at the entity pair level in the popular NYT10 dataset. |
Experiential Co-Learning of Software-Developing Agents (2024.acl-long)
Copied to clipboard
Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents. |
| Approach: | They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution. |
| Outcome: | The proposed framework enables agents to tackle unseen software-developing tasks more effectively. |
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)
Copied to clipboard
Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu
| Challenge: | Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data . |
| Approach: | They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference. |
| Outcome: | The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets. |
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)
Copied to clipboard
Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
| Challenge: | Large language models (LLMs) are capable of performing tasks but are likely to be misused. |
| Approach: | They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model . |
| Outcome: | The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts . |
Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models (2024.emnlp-main)
Copied to clipboard
Xinrong Zhang, Yingfa Chen, Shengding Hu, Xu Han, Zihang Xu, Yuanwei Xu, Weilin Zhao, Maosong Sun, Zhiyuan Liu
| Challenge: | Large language models (LLMs) are increasingly permeating daily lives and require real-time interactions that mirror human conversations. |
| Approach: | They propose to use time-division-multiplexing to process queries and responses pseudo-simultaneously. |
| Outcome: | The proposed model can listen to users while generating output and adjust to provide instant feedback. |
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work reserves the principle dimensions of query and document embeddings for building more efficient retrieval systems. |
| Approach: | They propose to use Conditional Autoencoder to compress high-dimensional embeddings to maintain the same embeddable distribution and better recover ranking features. |
| Outcome: | The proposed algorithm achieves comparable ranking performance with its teacher model and makes the retrieval system more efficient. |
UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs (2024.acl-demos)
Copied to clipboard
Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation platforms are complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. |
| Approach: | They propose a lightweight evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency that integrates models, data, and metrics into a unified evaluation workflow. |
| Outcome: | The proposed evaluation framework is lightweight, comprehensive, modular, and efficient. |
Quantifying Similarity between Relations with Fact Distribution (P19-1)
Copied to clipboard
| Challenge: | a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks. |
| Approach: | They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs . |
| Outcome: | The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes. |
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models (2025.acl-long)
Copied to clipboard
Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, WangYan WangYan, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi
| Challenge: | Large language models face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. |
| Approach: | They propose a training strategy for extending the context window of LLMs including impactful token analysis, position index transformation, and training optimization strategies. |
| Outcome: | Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size. |
Fully Hyperbolic Neural Networks (2022.acl-long)
Copied to clipboard
| Challenge: | Existing hyperbolic neural networks encode features in the hyperbolical space yet formalize most of their operations in the tangent space. |
| Approach: | They propose a fully hyperbolic framework to build hyperbolical networks based on the Lorentz model by adapting Lorentzer transformations to formalize essential operations of neural networks. |
| Outcome: | The proposed framework has better performance on four NLP tasks compared with existing hyperbolic models . |
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios. |
| Approach: | They propose a method to construct query-candidate pairs and build the largest LCR dataset to date, LEAD. |
| Outcome: | Experimental results show that the method can provide ample training signals for LCR models. |
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)
Copied to clipboard
Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang
| Challenge: | Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries. |
| Approach: | They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
| Outcome: | The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)
Copied to clipboard
Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources. |
| Approach: | They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins. |
| Outcome: | The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%. |
Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)
Copied to clipboard
Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim
| Challenge: | Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning. |
| Approach: | They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling. |
| Outcome: | The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. |
Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents. |
| Approach: | They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks. |
| Outcome: | The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark. |
HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles. |
| Approach: | They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles. |
| Outcome: | The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy. |
bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)
Copied to clipboard
Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu
| 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. |
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing dialog models can generate on-topic utterances but struggle to proactively switch topics. |
| Approach: | They propose a topic-shift aware dialog benchmark based on human topic shift annotations. |
| Outcome: | The proposed benchmark enables chatbots to generate topic-shift responses while still struggling to decide when to change topic. |
Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large reasoning models have demonstrated remarkable mathematical problem-solving abilities, but their true reasoning shortcomings are often hidden. |
| Approach: | They propose to leverage the rigor and methodological complexity of mathematical proofs as a diagnostic tool to expose hidden failures. |
| Outcome: | The proposed model evaluation exploits the rigor and complexity of proof problems to uncover 10 fine-grained errors. |
Won’t Get Fooled Again: Answering Questions with False Premises (2023.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are often easily deceived by tricky questions such as “How many eyes does the sun have?” . |
| Approach: | They annotate a FalseQA dataset containing 2365 human-written FPQs and find that PLMs are capable of discriminating FPqs by fine-tuning on moderate numbers. |
| Outcome: | The proposed model can discriminate on FPQs by fine-tuning on moderate numbers of examples and generate reasonable explanations for false premise questions. |
Learning to Generate Structured Output with Schema Reinforcement Learning (2025.acl-long)
Copied to clipboard
Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
| Challenge: | Recent advances in large language models have facilitated the development of intelligent applications like automatic web search (Qin et al., 2023) Several methods exist for generating JSON strings from LLMs, including Prompting but often miss certain schemas. |
| Approach: | They propose to use 40K different JSON schemas to assess models' ability to generate valid JSON outputs. |
| Outcome: | The proposed model improves both in generating JSON outputs and downstream tasks. |
Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Using partially-aligned data is an alternative way of solving the dataset scarcity problem. |
| Approach: | They propose a task to generate human-readable text for describing some given structured data enabling more interpretability. |
| Outcome: | The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
Copied to clipboard
Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
Fine-Grained Legal Argument-Pair Extraction via Coarse-Grained Pre-training (2024.lrec-main)
Copied to clipboard
| Challenge: | Current methods conceptualize LAE as a supervised sentence-pair classification problem and necessitate extensive manual annotations. |
| Approach: | They propose a model that focuses on fine-grained alignment of argument pairs building upon coarse-grain complaint-defense pairs. |
| Outcome: | The proposed model outperforms baseline models by 3.7 and 2.4 points on average. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
Copied to clipboard
Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can be fine-tuned to new tasks, but in multi-task scenarios, training imbalance and seesaw effect often arise. |
| Approach: | They propose a flexible fine-tuning framework that leverages asymmetric optimization among LoRA experts to reduce training imbalance and improve performance. |
| Outcome: | The proposed framework outperforms baseline methods in inter- and intra-task learning scenarios. |
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning (2021.acl-long)
Copied to clipboard
Yujia Qin, Yankai Lin, Ryuichi Takanobu, Zhiyuan Liu, Peng Li, Heng Ji, Minlie Huang, Maosong Sun, Jie Zhou
| Challenge: | Existing pre-training objectives do not explicitly model relational facts in text . Experimental results show that ERICA can improve typical PLMs on several language understanding tasks, including relation extraction, entity typing and question answering. |
| Approach: | They propose a contrastive learning framework ERICA to obtain a deep understanding of entities and relations in text. |
| Outcome: | The proposed framework can improve PLMs on several language understanding tasks, especially under low-resource settings. |
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents. |
| Approach: | They present a problem of cross-document relation extraction (CRE) using human annotations. |
| Outcome: | The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models. |
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)
Copied to clipboard
| Challenge: | Medical large vision-language models suffer from factual inaccuracies and unreliable outputs. |
| Approach: | They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources. |
| Outcome: | The proposed framework improves Med-LVLMs through heterogeneous knowledge sources. |
WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)
Copied to clipboard
| Challenge: | Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory . |
| Approach: | They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries . |
| Outcome: | The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries . |
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)
Copied to clipboard
Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English. |
| Approach: | They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries. |
| Outcome: | The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation. |
DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)
Copied to clipboard
Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored. |
| Approach: | They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python. |
| Outcome: | The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python. |
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention (D18-1)
Copied to clipboard
| Challenge: | Existing methods for relation extraction use knowledge graphs to automatically label training data . but, it suffers from the wrong labeling problem because not all sentences containing two entities can express their relations in KGs . |
| Approach: | They propose a distant supervision approach to automatically label training instances . they integrate hierarchical information of relations into distantly supervised relation extraction . |
| Outcome: | The proposed model outperforms baseline models on a large-scale dataset. |
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
Copied to clipboard
| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
| Approach: | They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data. |
| Outcome: | The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc. |
Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models (2025.findings-acl)
Copied to clipboard
You Li, Heyu Huang, Chi Chen, Kaiyu Huang, Chao Huang, Zonghao Guo, Zhiyuan Liu, Jinan Xu, Yuhua Li, Ruixuan Li, Maosong Sun
| Challenge: | Existing MLLMs still struggle to achieve precise grounding in multi-image scenarios. |
| Approach: | They propose a Chain-of-Thought framework that integrates single-image grounding with multi-image comprehension to address this challenge. |
| Outcome: | The proposed model outperforms existing models in multi-image grounding tasks by 24.94% and surpasses larger 70B models. |
LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation (2024.emnlp-demo)
Copied to clipboard
| Challenge: | Semi-structured interviews are a crucial method of data acquisition in qualitative research. |
| Approach: | They propose a semi-structured interview system that automates interview preparation, analysis and control by interviewers. |
| Outcome: | Experimental results show that LM-Interview performs comparable to human interviewers . the system can be used to analyze semi-structured interviews without interviewers' involvement . |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
Copied to clipboard
| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)
Copied to clipboard
| Challenge: | Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness. |
| Approach: | They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory. |
| Outcome: | The proposed method achieves significantly better performance than baseline methods. |
Event Detection with Trigger-Aware Lattice Neural Network (D19-1)
Copied to clipboard
| Challenge: | Event detection is a key part of event extraction, but there are two issues with word-based models in languages without natural delimiters, such as Chinese. |
| Approach: | They propose a framework that can solve the problem of word- trigger mismatch . they also use an external knowledge base to model polysemous characters and words . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two benchmark datasets and outperformed previous state- of-the art methods significantly. |
SolEval: Benchmarking Large Language Models for Repository-level Solidity Smart Contract Generation (2025.emnlp-main)
Copied to clipboard
| 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. |
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models that generate natural language responses for conversations degenerate dull and repetitive contents, leading to off-topic and useless responses. |
| Approach: | They propose a conversation generation model which leverages commonsense knowledge graphs to explicitly model conversation flows by grounding conversations to the concept space. |
| Outcome: | Experiments on Reddit conversations show that the proposed model generates more semantic and informative responses while using 70% fewer parameters. |
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations. |
| Approach: | They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. |
| Outcome: | The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations. |
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)
Copied to clipboard
| Challenge: | Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered . |
| Approach: | They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts. |
| Outcome: | The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script. |
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)
Copied to clipboard
Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu
| Challenge: | Existing code debugging benchmarks focus on the Code Repair stage of the code generation process. |
| Approach: | They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process. |
| Outcome: | The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5. |
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific domains. |
| Approach: | They propose a framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations. |
| Outcome: | The proposed framework improves multimodal LLMs through cross-modal alignment and multi-graph understanding. |
OpenKE: An Open Toolkit for Knowledge Embedding (D18-2)
Copied to clipboard
| Challenge: | Existing knowledge embedding tools are available for embeddable knowledge graphs. |
| Approach: | They propose a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. |
| Outcome: | The toolkit and pre-trained embeddings are available on http://openke.thunlp.org/. |
Fact Discovery from Knowledge Base via Facet Decomposition (N19-1)
Copied to clipboard
| Challenge: | Recent years have witnessed the emergence and growth of many large-scale knowledge bases (KBs) however, there are some issues unsettled towards enriching the KBs. |
| Approach: | They propose a framework that decomposes the discovery problem into several facet components and an auto-encoder component to estimate some facets of the fact. |
| Outcome: | The proposed framework achieves promising results on a benchmark dataset. |
Put It Back: Entity Typing with Language Model Enhancement (D18-1)
Copied to clipboard
| Challenge: | Existing Entity typing models suffer from noisy labels due to distant supervision . |
| Approach: | They propose to enhance existing entity typing models with language model enhancement to measure compatibility between context sentences and labels. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on benchmark datasets and is available on github. |
Large Language Models Are Still Misled by Simple Bias Ensembles (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks for large language models are constrained to datasets where each sample is manually injected with only one type of bias. |
| Approach: | They propose a multi-bias benchmark where each sample contains multiple types of biases. |
| Outcome: | The proposed benchmark shows that existing LLMs and debiasing methods perform poorly on this benchmark, highlighting the challenge of eliminating compounded biases. |
StateX: Enhancing RNN Recall via Post-training State Expansion (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that RNNs with large recurrent states are expensive to train . however, the ability to recall contextual information from long contexts is underperforms them in certain aspects. |
| Approach: | They propose a framework that expands the states of pre-trained RNNs by scaling them up to 1.3B . they use a recurrent architecture that compresses contextual information into a fixedsize state . |
| Outcome: | Experiments on models with up to 1.3B parameters show that StateX expands state sizes without incurring high post-training costs or compromising other capabilities. |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
Copied to clipboard
Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters. |
| Approach: | They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace. |
| Outcome: | The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance. |
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)
Copied to clipboard
| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made significant progress in utilizing tools, but their closed-source nature and high inference costs pose limitations on their adaptability. |
| Approach: | They propose a framework that performs task-solving by first creating a toolkit and then integrating the planning and calling of tools through a chain-of-solve approach. |
| Outcome: | The proposed model performs task-solving by harnessing Toolink's creativity and CoS ability on ChatGPT and finetunes the LLaMA-7B model. |
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow. |
| Approach: | They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition. |
| Outcome: | The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT. |
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning (2021.findings-acl)
Copied to clipboard
| Challenge: | Pretrained language models perform poorly under adversarial attacks due to the large search space. |
| Approach: | They propose a method to cover a much larger proportion of the attack search space by adding textual adversarial examples during training. |
| Outcome: | The proposed method covers a much larger proportion of the attack search space. |
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)
Copied to clipboard
Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size. |
| Approach: | They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding. |
| Outcome: | The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models. |
Learning from Context or Names? An Empirical Study on Neural Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets may leak shallow heuristics via entity mentions, thus contributing to the high performance on RE benchmarks. |
| Approach: | They propose an entity-masked contrastive framework for relation extraction to gain a deeper understanding on textual context and type information while avoiding rote memorization of entities. |
| Outcome: | The proposed framework improves the effectiveness and robustness of neural models in different RE scenarios. |
Few-Shot Charge Prediction with Discriminative Legal Attributes (C18-1)
Copied to clipboard
| Challenge: | Existing works on charge prediction perform well on high-frequency charges but are not capable of predicting few-shot charges with limited cases. |
| Approach: | They propose an attribute-attentive charge prediction model to infer attributes and charges simultaneously . they propose to use discriminative attributes as the internal mapping between fact descriptions and charges . |
| Outcome: | The proposed model outperforms baseline models on real-world datasets by more than 50% . the proposed model can predict the attributes and charges simultaneously . |
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases (2026.acl-long)
Copied to clipboard
Yida Cai, Ranjuexiao Hu, Huiyuan Xie, Chenyang Li, Yun Liu, Yuxiao Ye, Zhenghao Liu, Weixing Shen, Zhiyuan Liu
| Challenge: | Legal relations are an important analytical framework for dispute resolution in civil cases. |
| Approach: | They propose a comprehensive schema for legal relations in civil cases with hierarchical taxonomy and definitions of arguments. |
| Outcome: | The proposed schema shows that existing LLMs lack the ability to identify civil legal relations and performance improves on downstream tasks. |
Plug-and-Play Document Modules for Pre-trained Models (2023.acl-long)
Copied to clipboard
Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan, Yankai Lin, Zhiyuan Liu, Xiangyang Li, Zhonghua Li, Zhao Cao, Maosong Sun
| Challenge: | Large-scale pre-trained models have been widely adopted for document-oriented NLP tasks, such as question answering. |
| Approach: | They propose to decouple document encoding from downstream tasks by introducing a document plugin into the backbone of a PTM. |
| Outcome: | The proposed model can encode documents once and for all across different scenarios. |
ERNIE: Enhanced Language Representation with Informative Entities (P19-1)
Copied to clipboard
| Challenge: | Existing pre-trained language models rarely consider incorporating knowledge graphs (KGs) Existing models capture rich semantic patterns from plain text and can be fine-tuned to improve performance of NLP tasks. |
| Approach: | They propose to incorporate knowledge graphs into pre-trained language models to enhance language representation with external knowledge. |
| Outcome: | The proposed model can take full advantage of lexical, syntactic, and knowledge information simultaneously. |
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020.acl-main)
Copied to clipboard
| Challenge: | Legal Artificial Intelligence (LegalAI) focuses on applying artificial intelligence to help legal tasks. |
| Approach: | They introduce the history, current state, and future directions of research in LegalAI . they illustrate the tasks from the perspectives of legal professionals and NLP researchers . |
| Outcome: | The proposed system can reduce heavy and redundant work for legal professionals . it can also provide a reliable reference to those who are not familiar with the legal domain . |
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights . |
| Approach: | They propose a model with highlighting mechanism to assign greater attention weights to key phrases . they propose two structures of highlighting attention for each head and the multihead highlighting . experimental results show that their proposed model significantly outperforms the baseline model . |
| Outcome: | The proposed model outperforms the baseline models on a multi-news dataset. |
Modeling Semantic Compositionality with Sememe Knowledge (P19-1)
Copied to clipboard
| Challenge: | Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. |
| Approach: | They propose to incorporate sememes into SC models and employ them in learning multiword expressions. |
| Outcome: | The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge. |
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
| Approach: | They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies. |
| Outcome: | The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language. |
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following (2024.acl-long)
Copied to clipboard
| Challenge: | Embodied Instruction Following (EIF) is a crucial task in embodied learning . however, there is n'a unified understanding regarding the impact of various components on task performance . |
| Approach: | They propose a framework that delineates the core components essential for embodied learning tasks . they integrate a multi-agent design into the Planner component of their LLM-centric architecture . |
| Outcome: | OPEx delineates the core components essential for solving embodied learning tasks . integrating a multi-agent design into the Planner component of the LLM-centric architecture further elevates performance. |
IsOBS: An Information System for Oracle Bone Script (2020.emnlp-demos)
Copied to clipboard
| Challenge: | Oracle bone script (OBS) documents are the oldest continuously-used writing system in the world and are important for linguistic and historical research. |
| Approach: | They construct an information system for OBS to symbolize, serialize, and store OBS data at the character-level using efficient databases and retrieval modules. |
| Outcome: | The proposed system symbolizes, serializes, and stores OBS data at the character-level, based on efficient databases and retrieval modules. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
Copied to clipboard
Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web (2026.acl-long)
Copied to clipboard
Zhiyuan Yao, Zishan Xu, Yifu Guo, Zhiguang Han, Cheng Yang, Shuo Zhang, Weinan Zhang, Xingshan Zeng, Weiwen Liu
| Challenge: | Existing routers that use hardcoded tools are limited by scalability and generality bottlenecks. |
| Approach: | They propose a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. |
| Outcome: | The proposed pipeline can train routers with dynamic context understanding to create the plug-and-play Light Routing Agent. |
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models (2025.coling-main)
Copied to clipboard
Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, Maosong Sun
| Challenge: | Activation sparsity is a promising paradigm for accelerating model inference . few large language models achieve high activation spar and comparable performance . |
| Approach: | They propose a method to achieve activation sparsity and acceleration in large language models . they introduce ReLU activation and adopt progressive sparse regularization . |
| Outcome: | The proposed method achieves high activation sparsity and comparable model performance. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
Copied to clipboard
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning. |
| Approach: | They propose a framework that enables LLMs to create their own tools using documentation and code realization. |
| Outcome: | The proposed framework outperforms existing chain-of-thought, program-of thought, and tool-using baselines on MATH and TabMWP benchmarks. |
Rethinking Skip Connection with Layer Normalization (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to solve the optimization problem of deep neural networks are not linear, but can be used as a modulating mechanism between the input and output. |
| Approach: | They propose to use skip connection to adjust the scale of the input and output to improve the performance. |
| Outcome: | The proposed approach improves performance and convergence of deep neural networks and can be applied to machine translation and image classification datasets. |
LEGENT: Open Platform for Embodied Agents (2024.acl-demos)
Copied to clipboard
Zhili Cheng, Zhitong Wang, Jinyi Hu, Shengding Hu, An Liu, Yuge Tu, Pengkai Li, Lei Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing integrations of large language models and large multimodal models are limited . Existing platforms for developing embodied agents are limited and limited based on open-source software. |
| Approach: | They propose an open platform for developing embodied agents using LLMs and LMMs. |
| Outcome: | The proposed platform surpasses GPT-4V in embodied tasks with its model training on LEGENT data. |