Papers by Wei Xu
Course-Correction: Safety Alignment Using Synthetic Preferences (2024.emnlp-industry)
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Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Liu Yan, Tianwei Zhang, Wei Xu, Han Qiu
| Challenge: | Recent studies show that large language models generate harmful content, but the potential for generating harmful content is an escalating concern. |
| Approach: | They propose to fine-tune LLMs with preference learning to emphasize the preference for timely course-correction by using an automated pipeline. |
| Outcome: | The proposed model improves course-correction skills without affecting general performance and resists jailbreak attacks. |
REAM♯: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation (2021.findings-acl)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings. |
| Approach: | They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references . |
| Outcome: | The proposed model improves the reliability of reference-based metrics with augmented reference sets. |
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing data augmentation methods for event extraction are costly and time-consuming. |
| Approach: | They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model. |
| Outcome: | The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length . |
Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements (2024.acl-long)
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| Challenge: | Existing charge prediction datasets focus on single-defendant cases, but real-world cases involve multiple defendants. |
| Approach: | They propose a benchmark that encompasses legal cases involving multiple defendants . they develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. |
| Outcome: | The proposed model outperforms state-of-the-art models in predicting crime charges while providing corresponding rationales. |
Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
| Outcome: | The proposed approach outperforms previous unsupervised approaches by a large margin and is competitive with early supervised models. |
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals. |
| Approach: | They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step. |
| Outcome: | The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)
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| Challenge: | Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese. |
| Approach: | They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference. |
| Outcome: | The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics. |
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery (2022.emnlp-main)
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| Challenge: | Existing methods for finding out-of-domain intents suffer from in-domain overfitting problem . previous methods fail to transfer prior knowledge to downstream clustering . |
| Approach: | They propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents . they propose IND pre-training objective to learn discriminative features while maintaining intra-class diversity . |
| Outcome: | The proposed framework improves on three benchmark datasets. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
TURING: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface (2021.acl-demo)
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Peng Xu, Wenjie Zi, Hamidreza Shahidi, Ákos Kádár, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao
| Challenge: | Existing text-to-SQL semantic parsers cannot achieve high accuracy in cross-database setting . TURING is a NLDB system that can be used to democratize data-driven insights for non-technical users . |
| Approach: | They propose a TURING system that provides high-precision natural language explanations of SQL queries in a beam. |
| Outcome: | The proposed system achieves 75.1% execution accuracy and 78.3% top-5 beam execution accuracy on the Spider validation set. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)
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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang
| Challenge: | Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks. |
| Approach: | They propose a unified model to represent various multi-modal tasks using a single representation. |
| Outcome: | The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability. |
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)
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| Challenge: | MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. |
| Approach: | a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests . |
| Outcome: | the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements. |
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization (2026.acl-long)
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Tian Xueyun, MingHua Ma, Bingbing Xu, Nuoyan Lyu, Wei Li, Heng Dong, Zheng Chu, Yuanzhuo Wang, Huawei Shen
| Challenge: | Recent studies show that supervised fine-tuning (SFT) is a common approach for reasoning in large language models. |
| Approach: | They propose to use supervised fine-tuning (SFT) on chain-of-thought trajectories demonstrations . they find that incorporating negative traxories yields substantial OOD generalization gains . |
| Outcome: | The proposed scheme yields 5.51% OOD gain over positive-only training. |
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021.naacl-main)
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| Challenge: | Existing methods provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. |
| Approach: | They propose a method to extract supporting facts from irregular EMR without external knowledge bases by constructing a hierarchical graph network and using it to obtain causal relationship between multi-granularity features and diagnosis results. |
| Outcome: | The proposed method diagnoses four types of EMR correctly and provides accurate supporting facts for the results. |
Optimizing Deeper Transformers on Small Datasets (2021.acl-long)
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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao
| Challenge: | a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets. |
| Approach: | They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch . |
CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development (2026.acl-long)
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| Challenge: | Existing LLMs model overly capable learners who over-apply feedback, resulting in pedagogically implausible behavior. |
| Approach: | They propose a framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision. |
| Outcome: | The proposed model produces distinguishable proficiency levels and is consistent with instructional theories. |
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (P19-1)
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| Challenge: | Existing models for article comment generation are too long and often result in general and irrelevant comments. |
| Approach: | They propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. |
| Outcome: | The proposed model can generate coherent and informative comments compared with several strong baseline models. |
DebateQA: Evaluating Question Answering on Debatable Knowledge (2026.findings-eacl)
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
| Approach: | They propose to use a dataset of 2,941 debatable questions to assess their ability to provide comprehensive answers to inherently debatably asked questions. |
| Outcome: | The proposed model performs well on 2,941 debatable questions accompanied by human-annotated partial answers that capture a variety of perspectives. |
An Encoding Strategy Based Word-Character LSTM for Chinese NER (N19-1)
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| Challenge: | Existing word-based model can not be trained in batches due to its DAG structure. |
| Approach: | They propose a lattice model that integrates word information into the start or end characters of a word and integrates it into a fixed-sized representation for efficient batch training. |
| Outcome: | The proposed model outperforms other state-of-the-art models on benchmark datasets and shows that it can be trained in batches without a shortcut path. |
DTDES-KGE: Dual-Teacher Knowledge Distillation with Distinct Embedding Spaces for Knowledge Graph Embeddings (2025.findings-emnlp)
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| Challenge: | Existing knowledge distillation methods rely on a single teacher embedding space . existing methods overlook valuable complementary knowledge from teachers in distinct embeddable spaces. |
| Approach: | They propose a knowledge distillation framework that leverages dual teachers in embedding spaces to enhance performance. |
| Outcome: | The proposed framework significantly improves knowledge distillation performance by leveraging dual teachers in distinct embedding spaces. |
SA-DETR:Span Aware Detection Transformer for Moment Retrieval (2025.coling-main)
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| Challenge: | Moment Retrieval aims to locate video segments related to text. |
| Approach: | They propose a method that leverages the importance of instance related span anchors . they initialize span anchor using instance related fuse token and supervise them with GT labels . |
| Outcome: | The proposed method achieves competitive results on QVHighlights, Charades-STA and TACoS. |
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (2026.findings-eacl)
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| Challenge: | Existing LLM-based agents struggle with low diversity and suboptimal code generation. |
| Approach: | They propose an approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. |
| Outcome: | The proposed approach shows a 4% improvement in performance compared to the strong open-source AutoML agents. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
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| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)
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| Challenge: | Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias. |
| Approach: | They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. |
| Outcome: | The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results. |
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (D19-1)
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| Challenge: | Existing event extraction methods are limited to extract event arguments within the sentence scope. |
| Approach: | They propose a model which generates an entity-based directed acyclic graph to fulfill document-level EE effectively. |
| Outcome: | The proposed model can generate entity-based directed acyclic graph to fulfill document-level EE effectively. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation (2025.emnlp-main)
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| Challenge: | Current studies evaluate LLMs on explicit false statements, overlooking how misinformation manifests subtly as unchallenged premises in real-world interactions. |
| Approach: | They propose to use EchoMist to analyze implicit misinformation from diverse sources . they also investigate two mitigation methods to enhance LLMs’ capability to counter implicit mis information. |
| Outcome: | The proposed model fails to detect false premises and generate counterfactual explanations. |
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
| Approach: | They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning. |
| Outcome: | Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines. |
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)
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| Challenge: | Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account. |
| Approach: | They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework. |
| Outcome: | The proposed method is able to group new unknown intents into different clusters, enabling future development of the system. |
ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)
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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu
| Challenge: | Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance . |
| Approach: | They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal. |
| Outcome: | The proposed method surpasses state-of-the-art methods for task-specific instruction tuning. |
ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks. |
| Approach: | They propose a framework which uses pre-training datasets to rewrite instructions and generate negative responses to preserve the performance of the original LLM. |
| Outcome: | The proposed framework can erase the pre-training data while maintaining the performance of the original model. |
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)
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| Challenge: | Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items . |
| Approach: | They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance. |
| Outcome: | The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency. |
Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)
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| Challenge: | Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words. |
| Approach: | They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously. |
| Outcome: | The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset. |
Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers. |
| Approach: | They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations. |
| Outcome: | The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models. |
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation (2023.acl-long)
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| Challenge: | Large-scale pre-trained vision-language models have recently achieved tremendous success on a wide range of cross-modal tasks. |
| Approach: | They propose a new framework for a semantically-aware contrastive learning that minimizes the MI between false negative and positive samples . |
| Outcome: | The proposed framework minimizes the MI between false negative samples and positive samples even though they share similar semantics. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
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| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Demystifying Small Language Models for Edge Deployment (2025.acl-long)
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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu
| Challenge: | Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things. |
| Approach: | They propose to use SLMs to build and optimize a set of small language models that are publicly accessible. |
| Outcome: | The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
Disentangled Information Bottleneck for Adversarial Text Defense (2025.emnlp-main)
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| Challenge: | Existing studies have proven that these deep models are super vulnerable to adversarial examples, which are slightly modified inputs. |
| Approach: | They propose a novel text defense method that separates the robust and non-robust features with a disentangled two-line framework rather than the one-line compression network in IB. |
| Outcome: | The proposed method outperforms six baselines on four datasets with accuracy improvements ranging from 3.8% to 20.7%. |
Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)
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| Challenge: | Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation. |
| Approach: | They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English. |
| Outcome: | The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects. |
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression (2021.emnlp-main)
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| Challenge: | Recent studies on compression of pretrained language models usually use preserved accuracy as the metric for evaluation. |
| Approach: | They propose two new metrics that measure how closely a compressed model mimics the original model. |
| Outcome: | The proposed metrics measure how closely a compressed model (i.e., student) mimics the original model (e.g., teacher). |
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification (2024.acl-long)
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Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. |
| Approach: | They propose a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
| Outcome: | The proposed framework characterizes and recovers simplification-induced information loss in form of question-and-answer (QA) pairs. |
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
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| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
Code and Named Entity Recognition in StackOverflow (2020.acl-main)
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| Challenge: | StackOverflow has 15 million programming related questions written by 8.5 million users . however, there is a lack of fundamental NLP resources and techniques for identifying software-related named entities within natural language sentences. |
| Approach: | They propose a named entity recognition corpus for the computer programming domain with 15,372 sentences annotated with 20 fine-grained entity types. |
| Outcome: | The proposed model improves on 152 million sentences from StackOverflow and achieves 79.10 F-1 score for code and named entity recognition. |
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)
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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song
| Challenge: | Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning. |
| Approach: | They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized. |
| Outcome: | The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized . |
Multilingual Simplification of Medical Texts (2023.emnlp-main)
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Sebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh Ramanathan, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Existing work on medical text simplification has focused on monolingual settings . important findings in medicine are typically presented in technical, jargon-laden language . text simulating models can generate viable simplified texts, but there are outstanding challenges . |
| Approach: | They propose a dataset for medical text simplification in four languages . they evaluate fine-tuned and zero-shot models across these languages based on human assessments and analyses . |
| Outcome: | The proposed dataset evaluates models in English, Spanish, French, and Farsi . it shows that the models can generate viable simplified texts, but there are challenges . |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
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| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |
Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks (2026.acl-demo)
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| Challenge: | Existing methods are designed for specific settings, each with its own set of challenges. |
| Approach: | They propose a unified, modular, and extensive Text-to-SQL framework . it proposes a universal execution paradigm and a multi-actor collaboration mechanism . |
| Outcome: | Squrve proposes a unified, modular, and extensive Text-to-SQL framework . the framework outperforms existing methods on widely adopted benchmarks . |
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
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| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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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. |
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge (2021.naacl-main)
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| Challenge: | Cant is important for understanding advertising, comedies and dogwhistle politics . currently, there are very few resources available for the research of cant . |
| Approach: | They propose a large and diverse dataset for creating and understanding cant from a computational linguistics perspective. |
| Outcome: | The proposed dataset can be used to test word embedding similarity and pretrained language models. |
LLM-as-Scheduler: Agentic Workflow Dynamic Scheduling (2026.acl-long)
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| Challenge: | Experiments show that LAS cuts token usage by 43% and reduces end-to-end latency by more than 36%, while causing at most a 1.4 percentage-point drop in accuracy compared with a strong fixed workflow. |
| Approach: | They propose a system that dynamically chooses the right workflow for each query. |
| Outcome: | Experiments show that LAS cuts token usage by 43% and reduces end-to-end latency by more than 36% while causing at most a 1.4 percentage-point drop in accuracy compared with a strong fixed workflow. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
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| 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. |
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification (2024.acl-long)
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| Challenge: | Language models have demonstrated remarkable performance in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. |
| Approach: | They propose a method to assess concept bias in models during fine-tuning and in-context learning using ChatGPT. |
| Outcome: | The proposed method outperforms token removal approaches and is validated through extensive testing. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
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| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
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| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)
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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou
| Challenge: | Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. |
| Approach: | They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn . |
| Outcome: | The proposed benchmark is very challenging for state-of-the-art models, it is found. |
“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents (2025.emnlp-main)
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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Ke Xu, Minlie Huang, Chao Zhang, Han Qiu
| Challenge: | Large language models (LLMs) exhibit prompt leakage vulnerabilities, raising intellectual property and confidentiality concerns. |
| Approach: | They use probing techniques to capture LLMs’ intent-related internal representations and show that they internalize prompt leakage intents in their hidden states before generating tokens. |
| Outcome: | The proposed probes achieve 90%+ AUROC across all tested models, even when applied to new system prompts and attacks. |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)
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Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su
| Challenge: | Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context. |
| Approach: | They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration. |
| Outcome: | Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness. |
Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)
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| Challenge: | Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias. |
| Approach: | They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints. |
| Outcome: | The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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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. |
Improving Large-scale Paraphrase Acquisition and Generation (2022.emnlp-main)
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| Challenge: | Existing Twitter-based paraphrase datasets lack quality definitions for identification and generation tasks. |
| Approach: | They propose to use two separate definitions of paraphrase for identification and generation tasks in existing Twitter-based paraphrase datasets. |
| Outcome: | The proposed model achieves state-of-the-art performance of 84.2 F1 for automatic paraphrase identification compared to other models fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT. |
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning (2022.emnlp-main)
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| Challenge: | Existing methods to detect out-of-domain (OOD) intents ignore alignment between representation learning and scoring function, limiting performance. |
| Approach: | They propose a unified neighborhood learning framework to detect OOD intents . they propose to align representation learning with scoring function . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Granular Privacy Control for Geolocation with Vision Language Models (2024.emnlp-main)
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| Challenge: | Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. |
| Approach: | They develop a benchmark to evaluate the ability of VLMs to moderate geolocation dialogues with users. |
| Outcome: | a new benchmark evaluates the ability of VLMs to moderate geolocation conversations with users. |
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models (2024.findings-emnlp)
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| Challenge: | AC-EVAL is a benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. |
| Approach: | They propose a benchmark to assess the advanced knowledge and reasoning capabilities of LLMs in ancient Chinese. |
| Outcome: | AC-EVAL aims to assess the comprehension of ancient Chinese texts . the benchmark covers 13 tasks covering historical facts, geography, social customs, art, philosophy, classical poetry and prose. |
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)
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| Challenge: | Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating. |
| Approach: | They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages. |
| Outcome: | The proposed benchmark outperforms pre-trained models in Russian in zero-shot cross-lingual transfer to low-resource languages. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)
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Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu
| Challenge: | Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. |
| Approach: | They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding. |
| Outcome: | InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks. |
Preemptive Answer “Attacks” on Chain-of-Thought Reasoning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. |
| Approach: | They propose a scenario where the LLM obtains an answer before engaging in reasoning, and propose two measures to bolster the robustness of this approach. |
| Outcome: | The proposed model significantly impairs its reasoning capability across various CoT methods and a broad spectrum of datasets. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
Improved Instruction Ordering in Recipe-Grounded Conversation (2023.acl-long)
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| Challenge: | In this paper, we explore the task of instructional dialogue and focus on the cooking domain. |
| Approach: | They propose to explore two auxiliary subtasks to support response generation with improved instruction grounding by incorporating user intent and instruction state information into the model. |
| Outcome: | The proposed model lacks understanding of user intent and inability to track instruction state (i.e., which step was last instructed) incorporating user intent information helps the response generation model mitigate the incorrect order issue. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding (2024.acl-long)
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| Challenge: | Existing large language models struggle to capture some language styles without fine-tuning. |
| Approach: | They propose to meta-trained LLMs based on representative lexicons to recognize new styles they have not been fine-tuned on. |
| Outcome: | The proposed method improves zero-shot transfer across styles on 13 established and 63 novel tasks generated with LLMs. |
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)
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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks. |
| Approach: | They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation. |
| Outcome: | EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% . |
Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)
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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu
| Challenge: | Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model. |
| Approach: | They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures. |
| Outcome: | The proposed framework yields significant performance gains on Twitter and other platforms. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph (2022.aacl-main)
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| Challenge: | Existing knowledge graph question answering methods only search for the answer in a large knowledge graph. |
| Approach: | They propose to partition retrieved knowledge subgraphs into smaller sub-KSGs and then use a graph-augmented learning to rank method to select the top-ranked sub-kSGs. |
| Outcome: | The proposed method can capture global interactions in question and subgraphs and local interactions on the full KSG and top-ranked sub-KSGs respectively. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
Reinforcement Learning with Token-level Feedback for Controllable Text Generation (2024.findings-naacl)
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| Challenge: | Existing methods for controllable text generation are guided by coarse-grained feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. |
| Approach: | They propose a reinforcement learning algorithm which formulates TOken-LEvel rewards for controllable text generation and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. |
| Outcome: | The proposed algorithm can achieve superior performance on single-attribute and multi-attract control tasks. |
SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)
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| Challenge: | Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge. |
| Approach: | They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information. |
| Outcome: | The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights. |
Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support (2025.emnlp-industry)
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Cen Zhao, Tiantian Zhang, Hanchen Su, Yufeng Zhang, Shaowei Su, Mingzhi Xu, Yu Liu, Wei Han, Jeremy Werner, Claire Na Cheng, Yashar Mehdad
| Challenge: | Existing offline approaches to improve an LLM-based customer support system rely on batch annotations. |
| Approach: | They propose an agent-in-the-loop framework that integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. |
| Outcome: | The proposed framework reduces retraining cycles from months to weeks by integrating four key types of annotations directly into live customer operations. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)
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| Challenge: | Existing methods for news comment generation have not been well studied. |
| Approach: | They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network. |
| Outcome: | The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets. |
Neural Response Generation with Meta-words (P19-1)
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| Challenge: | Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP . |
| Approach: | They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller. |
| Outcome: | The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy. |
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)
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| Challenge: | Existing process annotation approaches are computationally expensive. |
| Approach: | They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree. |
| Outcome: | The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench. |
Character-Based Neural Networks for Sentence Pair Modeling (N18-2)
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| Challenge: | Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification and semantic textual similarity. |
| Approach: | They propose to use subwords to represent sentences without pretrained word embeddings . they find that subword models can achieve new state-of-the-art results without pretraining . |
| Outcome: | The proposed models can achieve state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification. |
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion (2024.acl-long)
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| Challenge: | Existing text-to-image diffusion models generate images from text prompts due to inherent brevity of textual descriptions . however, the ability to accurately synthesize images with intricate details, such as specific entities or scenes, is limited due to the inherent bribery of text descriptions. |
| Approach: | They propose a multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs. |
| Outcome: | The proposed framework excels in both text-to-image generation and zero-shot subject-driven synthesis. |
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization (2025.emnlp-demos)
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| Challenge: | PromptSculptor automates the iterative prompt optimization process for Text-to-Image models . previous work focused on generating detailed, high-quality prompts based on user feedback . |
| Approach: | They propose a framework that decomposes a task into four specialized agents . they use Chain-of-Thought reasoning to transform a short, vague user prompt into a comprehensive, refined prompt. |
| Outcome: | The proposed framework significantly improves output quality and reduces iterations needed for user satisfaction. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications. |
| Approach: | They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks. |
| Outcome: | The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings. |
MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)
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| Challenge: | Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase. |
| Approach: | They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs . |
| Outcome: | The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase. |
Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration (2024.emnlp-main)
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| Challenge: | Existing methods for generating large language models have been criticized for their complexity and instability. |
| Approach: | They propose a value-based calibration method to better align Large Language Models with human preferences. |
| Outcome: | The proposed method surpasses existing methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings. |
Nuclear Deployed!: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes domains, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) . |
| Approach: | They propose a framework that is carefully constructed to effectively and naturally expose catastrophic risks in high-stakes domains such as CBRN. |
| Outcome: | The proposed framework exposes LLM agents to catastrophic behaviors and deception without being deliberately induced. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
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| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
| Approach: | They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity. |
| Outcome: | The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1. |
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)
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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). |
| Approach: | a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance. |
| Outcome: | a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models . |
Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024.findings-acl)
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| Challenge: | Existing factor mining models are inefficient and inefficient, resulting in a significant challenge to extract interpretable factors. |
| Approach: | They propose a model that integrates the strengths of both neural and symbolic models for factor mining. |
| Outcome: | The proposed model surpasses the SOTA RankIC and RankICIR in predicting S&P 500 returns on real-world stock market data. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
| Outcome: | The proposed framework improves empathetic response generation by incorporating emotion cause information into the model. |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)
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| Challenge: | Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. |
| Approach: | They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach. |
| Outcome: | The proposed model improves performance on multiple datasets and compares with existing models. |
Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)
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| Challenge: | a large number of natural language processing tasks are generated with specially designed architectures. |
| Approach: | They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning . |
| Outcome: | The proposed model performs comparable to state-of-the-art models on 10 tasks . it also shows that it can analyze differences and similarities in how the model handles different tasks compared to other models . |
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge . |
| Approach: | They propose a benchmark to connect theoretical foundations with practical business knowledge and applications. |
| Outcome: | The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business . |
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer (2021.acl-long)
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| Challenge: | Existing BERT-based pre-trained language models achieve high performance on many downstream tasks, but native derived sentence representations are collapsed and thus poor performance on semantic textual similarity (STS) tasks. |
| Approach: | They propose a framework for self-supervised Sentence Representation Transfer that adopts contrastive learning to fine-tune BERT in an unsupervised way. |
| Outcome: | The proposed framework improves on the BERT-derived representations by 8% on STS datasets and shows robustness in data scarcity scenarios. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
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Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
High-Order Semantic Alignment for Unsupervised Fine-Grained Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing studies focus on learning global or local correspondence, but lack fine-grained local-global alignment. |
| Approach: | They propose a High Order Semantic Alignment (HOSA) model that can provide complementary and comprehensive semantic clues to infer correlation scores. |
| Outcome: | The proposed model outperforms state-of-the-art models in retrieving the most relevant results. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
| Outcome: | Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete. |
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models (2025.emnlp-main)
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Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao
| Challenge: | Recent advances in multi-modal large language models have demonstrated remarkable capabilities in multimodal understanding, reasoning, and interaction. |
| Approach: | They propose a method that effectively aligns and integrates multi-scale knowledge of objects . they use a pipeline that provides over 300K essential training data to enhance alignment . |
| Outcome: | The proposed method effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Neural semi-Markov CRF for Monolingual Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment. |
| Approach: | They propose a neural semi-Markov CRF alignment model which unifies word and phrase alignments through variable-length spans. |
| Outcome: | The proposed model outperforms existing models on in-domain and out-of-domain evaluations and a QA-based benchmark with human annotations. |
arXivEdits: Understanding the Human Revision Process in Scientific Writing (2022.emnlp-main)
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| Challenge: | a new computational framework is developed to study text revision in scientific writing . authors propose a method to extract revision at document-, sentence-, and word-levels . |
| Approach: | They propose a computational framework for studying text revision in scientific writing . arXivEdits is an annotated corpus of 751 full papers from arX . authors propose to use sentence alignment, fine-grained edits and intents to extract revision . |
| Outcome: | The proposed framework can be used to study revision in scientific writing. |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
Pre-train or Annotate? Domain Adaptation with a Constrained Budget (2021.emnlp-main)
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| Challenge: | Recent work shows that pre-training in-domain language models can boost performance when adapting to a new domain. |
| Approach: | They propose to combine annotation and pre-training to maximize performance under budget constraints. |
| Outcome: | The proposed approach is based on the annotation cost of three procedural text datasets and pre-training cost of 3 in-domain language models. |
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. |
| Approach: | They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy. |
| Outcome: | The proposed model surpasses all baselines on various logical reasoning benchmarks. |
Feedback Is The Key for Automated Survey Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a promising foundation for literature surveys, but guiding them to generate accurate, reliable content remains a fundamental challenge. |
| Approach: | They propose a feedback-driven framework that incorporates feedback across three dimensions: outline feedback for structural clarity, citation feedback for evidence validation, and content feedback for readability and analytical depth. |
| Outcome: | The proposed framework significantly improves both citation and content quality, demonstrating feedback as the critical mechanism for automatic survey generation. |
FAKTA: An Automatic End-to-End Fact Checking System (N19-4)
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| Challenge: | Existing studies have investigated individual components of fact checking process but none offer such a capability. |
| Approach: | They propose a framework that integrates various components of a fact-checking process. |
| Outcome: | The proposed framework integrates various components of a fact-checking process to predict the factuality of claims and provide evidence at the document and sentence level to explain its predictions. |
CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation (2025.coling-main)
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| Challenge: | Existing methods for generating human-like dialogues lack implicit correlations among factors . different factors may alternately dominate token-level response generation during decoding . |
| Approach: | They propose a framework that can model complex multiple interaction factors to generate human-like conversations. |
| Outcome: | The proposed framework generates human-like conversations with multiple factors compared to state-of-the-art methods . et al. show that the proposed framework produces superior results over existing methods compared with the state- of-the art methods based on multiple datasets . |
Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)
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| Challenge: | Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs . |
| Approach: | They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information . |
| Outcome: | The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set. |
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)
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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan
| Challenge: | Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost . |
| Approach: | They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation . |
| Outcome: | GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%. |
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods. |
| Approach: | They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map. |
| Outcome: | The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021. |
Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)
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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model. |
| Approach: | They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering. |
| Outcome: | The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies. |
Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)
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| Challenge: | Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem. |
| Approach: | They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models. |
| Outcome: | The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks. |
Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking? (2025.findings-acl)
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| Challenge: | Existing jailbreak attacks fail against reasoning models enhanced by Chain-of-Thought (CoT) reasoning. |
| Approach: | They propose a jailbreak method that uses Chain-of-Thought reasoning to reduce harmfulness from jailbreaking. |
| Outcome: | The proposed jailbreak method performs well against open AI models and deepseek-R1 reasoning models. |
From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding (2025.emnlp-industry)
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Xiangfeng Wang, Xiao Li, Yadong Wei, null Songxueyu, Yang Song, null Xiaxiaoqiang, Fangrui Zeng, Zaiyi Chen, null Liuliu, Gu Xu, Tong Xu
| Challenge: | Existing methods for video editing rely on textual cues from ASR transcripts and segment selection, often neglecting rich visual context. |
| Approach: | They propose a human-inspired automatic video editing framework that leverages multimodal narrative understanding to address these limitations. |
| Outcome: | The proposed framework outperforms existing baselines across general and advertisement-oriented editing tasks. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
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| 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. |
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2021.acl-long)
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Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou
| Challenge: | Existing pre-training tasks for text and layout are effective in visually-rich document understanding tasks. |
| Approach: | They propose to combine pre-training tasks with a multi-modal model to model interaction between text, layout and image in a single multi-module framework. |
| Outcome: | The proposed model outperforms LayoutLM by a large margin on visual-rich document understanding tasks. |
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning (2025.coling-main)
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| Challenge: | Low-rank adaptation and its variants have been popular due to their ability to avoid excessive inference costs. |
| Approach: | They propose a low-rank adaptation method that enables high-rank updates with low costs while leveraging semantic and linguistic information inherent in pre-trained weight. |
| Outcome: | The proposed method outperforms LoRA and other fine-tuning methods across tasks with less trainable parameters. |
Speaker or Listener? The Role of a Dialog Agent (2020.findings-emnlp)
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| Challenge: | chitchat bots are designed as a passive listener to answer what people ask . however, this passive and relatively simple response mechanism consumes the interest of human beings rapidly. |
| Approach: | They propose a model to transform a passive dialogue agent into a speaker by using external knowledge to express proactivity. |
| Outcome: | The proposed model obtains competitive results on the automatic and manual metrics and improves fluency and engagement of the chatbot significantly. |
Introducing Semantics into Speech Encoders (2023.acl-long)
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Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution (2026.findings-acl)
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Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen, Jingjing Xu
| Challenge: | SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. |
| Approach: | They propose a two-phase training recipe that decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. |
| Outcome: | The proposed model achieves a 60.2% score on the SWE-bench Verified benchmark and is in the top-tier performance bracket of much larger models. |
LayoutReader: Pre-training of Text and Layout for Reading Order Detection (2021.emnlp-main)
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| Challenge: | Existing methods for reading order detection are too laborious to annotate large datasets. |
| Approach: | They propose to use a large-scale dataset to annotate reading order information for document images . they use XML metadata to capture the reading order of WORD documents . |
| Outcome: | The proposed model performs almost perfectly in reading order detection and improves both open-source and commercial OCR engines in ordering text lines in their results. |
DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)
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| Challenge: | Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information. |
| Approach: | They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation . |
| Outcome: | The proposed model trains on DocBank accurately recognize layout information for a variety of documents. |
Visualization Recommendation with Prompt-based Reprogramming of Large Language Models (2024.acl-long)
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| Challenge: | Traditional visualization recommendations require extensive manual maintenance and yet fail to fully comprehend tabular data. |
| Approach: | They propose a hierarchical table prompt-based reprogramming framework that integrates tabular data into LLMs through a strategically crafted prompt learning method. |
| Outcome: | The proposed framework achieves state-of-the-art performance and will be made publicly available upon acceptance. |
SGM: Sequence Generation Model for Multi-label Classification (C18-1)
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| Challenge: | Existing methods ignore the correlations between labels and different parts of the text can contribute differently for predicting different labels. |
| Approach: | They propose to view the multi-label classification task as a sequence generation problem and apply a decoder-based sequence generation model to solve it. |
| Outcome: | The proposed methods outperform previous work by a substantial margin. |
Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization (2024.acl-long)
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| Challenge: | Existing methods for WS-NLVL rarely consider complex temporal relations enclosing the language query, yielding illogical predictions. |
| Approach: | They propose a plug-and-play method to exploit temporal relations and logical rules for WS-NLVL. |
| Outcome: | The proposed method is able to retrieve the moment corresponding to a language query in a video with only video-language pairs utilized during training. |
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
Sampling Better Negatives for Distantly Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing supervised named entity recognition approaches rely on human annotations. |
| Approach: | They propose a method to select negative samples with high similarities with positive samples . they propose to use automatically labeled training data instead of human annotations . |
| Outcome: | The proposed method achieves consistent performance improvements on four distantly supervised NER datasets. |
One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling (2023.findings-emnlp)
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Ruoxue Ma, Jiarong Xu, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing studies on integrating online community to solve social problems have not fully utilized these three components and the relationship among them. |
| Approach: | They propose a framework that simultaneously considers communities, users, and texts and can easily connect with a variety of downstream tasks related to social media. |
| Outcome: | The proposed model can be used to perform violation detection, sentiment analysis, and community recommendation across multiple tasks. |
Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition (2024.findings-emnlp)
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| Challenge: | Recent studies have demonstrated remarkable performance on few-shot Named Entity Recognition tasks due to the high cost of obtaining high-quality labeled data. |
| Approach: | They propose to decompose the task into entity span detection and entity type classification using a type-independent entity span detector and then classify the detected spans based on their types. |
| Outcome: | The proposed method consistently yields improvements over two baseline approaches. |
Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
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| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
| Approach: | They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities . |
| Outcome: | The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text. |
On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena (2025.naacl-long)
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| Challenge: | Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. |
| Approach: | They propose a parallel Arabic-English benchmark of 58,086 entities associated with Arab and Western cultures and 367 masked natural contexts for entities. |
| Outcome: | The proposed model shows that LMs struggle in Arabic with entities that appear at high frequencies in pre-training, where entities can hold multiple word senses. |
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL (2026.findings-acl)
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| Challenge: | Existing NL2SQL systems rely on in-context learning with only correct examples . current test-time scaling methods often decompose questions arbitrarily, resulting in poor performance . |
| Approach: | They propose a structured decomposition and experience-aware self-correction framework for NL2SQL . they build a dynamic memory of successful queries and historical error–fix pairs . |
| Outcome: | The proposed framework achieves 68.5% execution accuracy on BIRD, setting new state of the art among open, zero-fine-tuning methods. |
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang
| Challenge: | Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature. |
| Approach: | They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding. |
| Outcome: | The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M . |
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)
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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui
| Challenge: | Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query. |
| Approach: | They propose a task where a textual premise is the background presumption on each source image. |
| Outcome: | The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories. |
DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)
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| Challenge: | Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. |
| Approach: | They propose a notebook-centric LLM agent framework for adaptive and robust data science automation. |
| Outcome: | The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models. |
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language (2021.findings-emnlp)
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| Challenge: | a particular type of bias is subjective bias, which introduces improper attitudes or presents a statement with the presupposition of truth. |
| Approach: | They propose to annotate a Wikipedia edits corpus with 4,000 sentence pairs to detect subjective bias. |
| Outcome: | The proposed dataset can be used as a research benchmark and generalize to multiple domains. |
End-to-End Optimization of LLM-Driven Multi-Agent Search Systems via Heterogeneous-Group-Based Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing multi-agent reinforcement learning methods depend on large critic networks to evaluate joint actions, leading to instability and high memory costs. |
| Approach: | They propose a method to optimize large language models for agent-specific roles . they propose combining agent-based frameworks with retrieval-augmented generation . |
| Outcome: | Experiments show that multi-agent group policy optimization outperforms baselines in task performance and computational efficiency. |
VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)
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| Challenge: | Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts. |
| Approach: | They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins . |
| Outcome: | The proposed method outperforms state-of-the-art models on six text matching benchmarks. |
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments (2023.acl-long)
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| Challenge: | Existing evaluations of human-in-the-loop systems to combat misinformation are often set up automatically using datasets that were retrospectively constructed. |
| Approach: | They propose a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them. |
| Outcome: | The proposed framework is based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. |
Revisiting the Evaluation of End-to-end Event Extraction (2021.findings-acl)
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| Challenge: | Existing EE research uses the role-averaged evaluation metric, but it is misleading to downstream applications. |
| Approach: | They propose two new evaluation metrics that explicitly penalize wrongly identified event arguments. |
| Outcome: | The proposed evaluation metrics improve the initial evaluation by 10% . the proposed training scheme is better than the existing one, the authors show . |
Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models (2023.acl-long)
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| Challenge: | Increasing the size of pre-trained models can consistently improve performance on downstream tasks after fine-tuning, as seen in studies based on BERT, RoBERTa, T5 and empirical scaling laws. |
| Approach: | They propose to use knowledge distillation to build a compact model with a fixed budget instead of annotating data and manually labeling it. |
| Outcome: | The proposed approach reduces inference costs by reducing costs by hiring annotators and labelling data. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| 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. |
NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms (2024.acl-long)
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| Challenge: | Prior work on temporal language change observed degradation when finetuning on older text and evaluating on newer data and named entities. |
| Approach: | They construct a benchmark to evaluate LLMs’ ability to generalize to neologisms with various natural language understanding tasks and model perplexity. |
| Outcome: | The proposed model performs better in downstream tasks and with later knowledge cutoff dates than models with earlier knowledge cut off dates. |
GeoRC: A Benchmark for Geolocation Reasoning Chains (2026.acl-long)
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Mohit Talreja, Joshua Diao, Jim James, Radu Casapu, Tejas Santanam, Ethan Mendes, Alan Ritter, Wei Xu, James Hays
| Challenge: | Vision Language Models (VLMs) are good at recognizing the global location of a photograph but are startlingly bad at explaining which image evidence led to their location prediction. |
| Approach: | They propose a benchmark for geolocation reasoning chains based on the global location prediction task in the popular GeoGuessr game. |
| Outcome: | The proposed benchmark compares LLM-as-a-judge and VLM-As-jumble strategies against human scoring. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs (2026.acl-long)
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| Challenge: | Existing jailbreak methods only use a single image, restricting the attack space . Existing frameworks only use single image to distribute harmful requests across multiple images . |
| Approach: | They propose a compositional jailbreak framework that leverages Distributed instruction, Multimodal evidence and a Number chain task to fully enhance the jailbreak performance. |
| Outcome: | The proposed framework achieves attack success rates of over 90% on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 . |
PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026.acl-long)
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Lei Wei, Xiao Peng, null Tt, Guannan Zhang, Chenhao Jiang, Hongyu Li, Lanbo Lin, Yuanwu Xu, Jiayao Liu, Kesu Wang, Bin Wang
| Challenge: | Large Language Model (LLM) agents struggle with ultra-long-horizon tasks requiring hundreds or thousands of interaction steps. |
| Approach: | They propose a framework that reconceptualizes context management as a Next Step Prediction problem. |
| Outcome: | The proposed framework improves task success rates and robust cross-lingual performance. |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
| Approach: | They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact. |
| Outcome: | The proposed model achieves better performance than previous approaches while requiring fewer parameters. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
| Approach: | They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides. |
| Outcome: | The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. |
SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? (2025.emnlp-main)
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Yao Dou, Michel Galley, Baolin Peng, Chris Kedzie, Weixin Cai, Alan Ritter, Chris Quirk, Wei Xu, Jianfeng Gao
| Challenge: | Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. |
| Approach: | They propose to use large language models to simulate users for automatic assistant evaluation. |
| Outcome: | The proposed model outperforms human evaluations on two interactive tasks and achieves Spearman’s of 0.7 on both tasks. |
Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence (2026.findings-acl)
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Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib, Ramez Kouzy, Wei Xu, Byron C Wallace, Junyi Jessy Li
| Challenge: | Existing models are overwhelmingly accurate when presented with counterfactual medical evidence . prior work explored conflicts between context and LLM parametric knowledge in the general domain . |
| Approach: | They construct a counterfactual medical QA dataset that requires models to answer clinical comparison questions with evidence from randomized controlled trials. |
| Outcome: | The proposed model overemphasizes the latter, and the model overestimates the latter. |
GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative Language Illustration (2024.emnlp-main)
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| Challenge: | Existing Large Language Models (LLMs) and multimodal models are unable to illustrate figurative language based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical domains. |
| Approach: | They propose a grounding-based method for metaphor illustration that integrates metaphorical knowledge into systematic instructions for existing large language models. |
| Outcome: | The proposed method is superior to existing LLMs, diffusion models, or their direct collaboration. |
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)
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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning. |
| Approach: | They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts. |
| Outcome: | The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube. |
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering (D19-1)
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| Challenge: | Existing approaches build universal paraphrasing or ranking models for whole questions . current approaches build a universal ranking model for the whole questions, which fails for complex, long-tail questions. |
| Approach: | They propose a new query generation approach based on frequent query substructures which helps rank existing query structures or build new query structures. |
| Outcome: | The proposed approach significantly outperforms existing models on two benchmark datasets. |
BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020.emnlp-main)
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| Challenge: | a novel approach to compress neural networks by progressive module replacement is proposed . a number of techniques have been proposed to compress pretraining and fine-tuning models . |
| Approach: | They propose a model compression approach that divides BERT into modules and builds their compact substitutes. |
| Outcome: | The proposed approach outperforms existing knowledge distillation approaches on GLUE benchmark . it is based on a model that divides the original BERT into several modules and builds their substitutes . |
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)
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| Challenge: | Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios. |
| Approach: | They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families. |
| Outcome: | The proposed method is superior to existing methods and can handle low-resource languages. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Investigating Learning Dynamics of BERT Fine-Tuning (2020.aacl-main)
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| Challenge: | Recent studies have shown that the fine-tuning process improves performance on downstream tasks. |
| Approach: | They propose two new pre-training tasks to improve the model performance on downstream tasks. |
| Outcome: | The proposed model achieves state-of-the-art on a wide array of NLP tasks. |
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
| Outcome: | The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions. |
ChatHF: Collecting Rich Human Feedback from Real-time Conversations (2024.emnlp-demo)
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| Challenge: | We present an interactive framework for chatbot evaluation that integrates configurable annotation within a chat interface. |
| Approach: | They propose an interactive framework for chatbot evaluation that integrates configurable annotation within a chat interface. |
| Outcome: | The proposed framework supports fine-grained error detection and human evaluation at the same time. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
Question Condensing Networks for Answer Selection in Community Question Answering (P18-1)
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| Challenge: | Community question answering (CQA) is a subtask of community question answering . previous researches ignored the difference between the two parts and concatenated them as the question representation . |
| Approach: | They propose a question condensing network that makes use of the subject-body relationship of community questions. |
| Outcome: | The proposed model outperforms existing models on two CQA datasets. |
Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)
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| Challenge: | Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts. |
| Approach: | They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale. |
| Outcome: | The proposed model generates coherent and coherent court views on a real-world criminal case dataset. |
Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting (2021.emnlp-main)
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| Challenge: | Existing text infilling objectives for pretrained language models require self-supervision by masking out tokens or spans in text. |
| Approach: | They propose to extend text infilling to a self-supervised sequence-to-sequence (Seq2Sequen) task. |
| Outcome: | The proposed task improves the model's performance on various natural language generation tasks. |
Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. |
| Approach: | They introduce a diagnostic benchmark and conduct a systematic evaluation of multilingual tool calling across Chinese, Hindi, and the low-resource language Igbo. |
| Outcome: | The proposed benchmarks show that multilingual tool calling fails despite correct intent understanding and tool selection. |
TEACH: A Contrastive Knowledge Adaptive Distillation Framework for Classical Chinese Understanding (2025.acl-long)
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| Challenge: | Traditional methods for processing classical Chinese segment language understanding into discrete tasks, which overlook crucial background information and reduce user engagement. |
| Approach: | They propose a framework that integrates word sense disambiguation with sentence translation to minimize hallucinations and improve semantic analysis. |
| Outcome: | The proposed framework integrates word sense disambiguation with sentence translation to minimize hallucinations and improve semantic analysis. |
HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)
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| Challenge: | relying on large language models for information has raised concerns about reliability and accuracy of outputs. |
| Approach: | They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process. |
| Outcome: | The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility. |
Low-Resource Response Generation with Template Prior (D19-1)
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| Challenge: | Existing open domain response generation models are limited to paired data, but are less explored in real-world applications. |
| Approach: | They propose to train a neural response generation model with unpaired data and paired data as prior. |
| Outcome: | The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available. |
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives (2023.emnlp-main)
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| Challenge: | Experimental results show that combining both proposed methods leads to a gain of 1.8 points compared to the strong baseline SimCSE configured with BERT base. |
| Approach: | They propose a method to deal with dropout noise and a dimension-wise contrastive learning objective to address feature corruption. |
| Outcome: | The proposed method achieves 1.8 points compared to the strong baseline SimCSE and 1.4 points for DiffCSE. |
MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain (2024.emnlp-main)
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| Challenge: | Using fine-grained readability measures is the first step towards making medical texts more accessible. |
| Approach: | They propose a dataset MedReadMe which measures sentences and complex spans with an annotation tool. |
| Outcome: | The proposed dataset covers 650 linguistic features and additional complex span features, and is compared against state-of-the-art methods using large language models. |
Multi-task Pairwise Neural Ranking for Hashtag Segmentation (P19-1)
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| Challenge: | Hashtags are used to add metadata to textual utterances, but their semantic content is difficult to infer as they often contain multiple tokens joined together. |
| Approach: | They propose to use a dataset of 12,594 hashtags to infer hashtag semantics . they propose to frame the problem as a pairwise ranking problem between candidate segmentations . |
| Outcome: | The proposed methods show 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. |
Random Smooth-based Certified Defense against Text Adversarial Attack (2024.findings-eacl)
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| Challenge: | Textual adversarial examples train models on the worst-case text generated by substituting words in original texts with synonyms, but due to the discrete word embedding representations, the large search space hinders the robust training efficiency. |
| Approach: | They propose to treat the word substitution as a continuous perturbation on the word embedding representation and apply random smooth techniques to approximate the word replacement operation. |
| Outcome: | The proposed method outperforms conventional methods and improves the robustness in training. |
Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding (2026.findings-acl)
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| Challenge: | Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors. |
| Approach: | They propose a large-scale dataset for **Perception-Aware Question Answering** that uses a hierarchical decoupling strategy to separate speech from environmental sounds and distinguishes among multiple speakers. |
| Outcome: | The proposed model improves on MMAU-mini, MMAR, and PAQA while maintaining comparable performance on multiple benchmarks. |
Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)
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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang
| Challenge: | Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications. |
| Approach: | They propose a framework that incorporates large language models to improve EA. |
| Outcome: | The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency. |
A Self-Evolving LLM Agent Framework for Role-Based Norm Compliance in Healthcare (2026.findings-acl)
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| Challenge: | Existing systems treat roles as static prompts and rely on one-shot safety filters . a self-evolving LLM agent is proposed that learns from role-based social experience . |
| Approach: | They propose a self-evolving LLM agent that learns from role-based social experience and explicitly models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
| Outcome: | The proposed agent learns from role-based social experience and models communicator-level individual traits informed by prior communication questionnaires and clinical literature. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)
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| Challenge: | Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order. |
| Approach: | They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality . |
| Outcome: | The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward. |
To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)
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| Challenge: | Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools. |
| Approach: | They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities. |
| Outcome: | The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT. |
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation (N18-1)
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| Challenge: | Existing models tend to memorize words instead of learning meaning of words . existing models tend not to model semantic information, resulting in incorrect sentences . |
| Approach: | They propose a novel model that generates words by querying distributed word representations . they evaluate model on two paraphrase-oriented tasks, namely text simplification and short abstractive summarization . |
| Outcome: | The proposed model outperforms the baseline model on two paraphrase-oriented tasks . it achieves state-of-the-art performance on these benchmark datasets . |
SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage (2025.findings-acl)
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| Challenge: | Existing methods to design sophisticated instructions for the LLM to follow, or rely on multiple iterations, could hinder the performance and efficiency of jailbreaks. |
| Approach: | They propose a simple assistive task linkage paradigm which masks harmful keywords within malicious queries and uses a masked language model task to encode the semantics of the mangled keywords. |
| Outcome: | The proposed paradigm can effectively circumvent LLM safeguards and elicit harmful responses. |
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (2020.emnlp-main)
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| Challenge: | Multilingual pre-trained Transformers have been shown to enable effective cross-lingual zero-shot transfer, but their performance on Arabic information extraction tasks is not well studied. |
| Approach: | They pre-train a bilingual BERT that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. |
| Outcome: | The pre-trained model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT in both the supervised and zero-shot transfer settings. |
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)
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| Challenge: | Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains. |
| Approach: | They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues. |
| Outcome: | The proposed model can be generalized to two multi-domain dialogue summarization datasets. |
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)
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| Challenge: | Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks. |
| Approach: | They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials. |
| Outcome: | The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs . |
| Approach: | They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning . |
| Outcome: | The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks. |
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
| Challenge: | Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge. |
| Approach: | They propose a modular open-source library to equip LLMs with external knowledge. |
| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
The Impact of Visual Information in Chinese Characters: Evaluating Large Models’ Ability to Recognize and Utilize Radicals (2025.naacl-long)
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| Challenge: | Existing studies have examined whether large language models and vision-language models can harness these sub-character features in Chinese through prompting. |
| Approach: | They establish a benchmark to evaluate large language models' understanding of visual elements in Chinese characters, including radicals, composition structures, strokes, and stroke counts. |
| Outcome: | The proposed model exhibits some, but still limited, knowledge of the visual elements in Chinese characters regardless of whether images of characters are provided. |
Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs (2025.findings-emnlp)
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| Challenge: | Multi-hop question answering is a challenging task that requires capturing information from multiple positions in multiple documents. |
| Approach: | They propose a framework for integrating text-based and triple-based paradigms that incorporates structured knowledge into large-scale question answering. |
| Outcome: | The proposed framework improves multi-hop question answering by incorporating structured knowledge into the models. |
Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are sensitive to the contextual position of information in input. |
| Approach: | They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. |
| Outcome: | Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures. |
PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain (2024.findings-emnlp)
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| Challenge: | Existing approaches to generate long music are inefficient and lack of structured representation. |
| Approach: | They propose a hierarchical discrete representation of audio for long audio-domain music generation using residual vector quantization on different levels of features. |
| Outcome: | The proposed method achieves competitive performance in terms of reconstruction quality and token per second (TPS) the proposed method facilitates training a language model that can generate well-structured long-form music for up to 3 minutes. |
Think and Recall: Layer-Level Prompting for Lifelong Model Editing (2025.emnlp-main)
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| Challenge: | Existing methods for lifelong model editing suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits. |
| Approach: | They propose a plug-and-play method for knowledge retrieval and storage, i.e., Layer-Level Prompting, which enables seamless and efficient lifelong model editing. |
| Outcome: | The proposed method outperforms existing methods on question answering and hallucination benchmarks across different LLMs. |
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling (2021.acl-long)
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| Challenge: | Existing joint models for multi-intent SLU only consider intent detection while ignoring slot filling task. |
| Approach: | They propose a non-autoregressive model for joint multiple intent detection and slot filling . their framework is 11.5 times faster than existing joint models . |
| Outcome: | The proposed model is 11.5 times faster than existing models and is faster than current models. |
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game (P18-1)
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| Challenge: | supervised language learning is limited by the ability of capturing mainly the statistics of training data. |
| Approach: | They propose to use conversational games to train agents to use new knowledge . they propose to mimic and reinforce conversational game and use it in one-shot fashion . |
| Outcome: | The proposed approach is able to acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. |
Knowledge Conflicts for LLMs: A Survey (2024.emnlp-main)
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| Challenge: | This survey examines knowledge conflicts for large language models (LLMs) this survey aims to shed light on strategies for improving the robustness of LLMs . |
| Approach: | They focus on three categories of knowledge conflicts: context-memory, inter-context, and intra-membry conflict. |
| Outcome: | The findings highlight the challenges faced by large language models when blending contextual and parametric knowledge. |
Android in the Zoo: Chain-of-Action-Thought for GUI Agents (2024.findings-emnlp)
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| Challenge: | Existing studies on large language models (LLMs) focus on the semantics of smartphone operations. |
| Approach: | They propose a large language model (LLM) which predicts a sequence of actions of API by analyzing past actions and visual observations. |
| Outcome: | The proposed model improves the prediction of actions on a zero-shot Android-In-The-Zoo dataset compared to previous models . |
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold (2022.naacl-main)
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Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
| Challenge: | Existing methods for OOD detection are based on labeled in-domain data . detecting out-of-domain (OOD) or unknown intents is challenging . |
| Approach: | They propose a novel reassigned contrastive learning method to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents. |
| Outcome: | The proposed method is effective for both aspects of overconfidence issues. |
Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space (2026.findings-acl)
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| Challenge: | Existing methods to remove undesirable data from Large Language Models suffer from cumulative catastrophic utility loss under continuous unlearning requests. |
| Approach: | They propose a method that leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process. |
| Outcome: | The proposed method achieves SOTA performance without a retained dataset. |
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification (D18-1)
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| Challenge: | Current lexical simplification approaches rely on heuristics and corpus level features that do not align with human judgment. |
| Approach: | They propose a human-rated word-complexity lexicon and a neural readability ranking model that uses human ratings to measure the complexity of any given word or phrase. |
| Outcome: | The proposed model performs better than state-of-the-art models for lexical simplification tasks and evaluation datasets. |
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols (N18-2)
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| Challenge: | Existing efforts to annotate natural language instructions into machine-readable formats are limited. |
| Approach: | They propose to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion into a machine-readable format. |
| Outcome: | The proposed corpus can be used to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. |
Evaluating Parameter Efficient Learning for Generation (2022.emnlp-main)
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Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams, Ryan Prenger, Wei Ping, Nayeon Lee, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Parameter efficient learning methods (PERMs) are gaining attention for their ability to adapt to a downstream task. |
| Approach: | They propose to use parameter efficient learning methods to improve model adaptation . they compare in-domain evaluations and generalizations to unseen domains and new datasets . |
| Outcome: | The proposed method outperforms finetuning and PERMs in in-domain evaluations. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Exploring Question Guidance and Answer Calibration for Visually Grounded Video Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for videoQA lack temporal localization labels, leading to inaccurate localization. |
| Approach: | They propose a Question-Guided and Answer-Calibrated TRansformer which guides and calibrates localization using question and option texts without localization labels. |
| Outcome: | The proposed model achieves comparable accuracy to large-scale pretrained models and leads in localization aspects. |
Decomposed Prompt Tuning via Low-Rank Reparameterization (2023.findings-emnlp)
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| Challenge: | Pre-trained language models have achieved remarkable performance on various tasks. |
| Approach: | They propose a decomposed prompt tuning approach that utilizes low-rank matrices to initialize the soft prompt. |
| Outcome: | The proposed method significantly reduces the number of trainable parameters while maintaining effectiveness. |
Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)
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| Challenge: | Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events. |
| Approach: | They propose a framework for event extraction which models the relations between arguments by an event matrix. |
| Outcome: | The proposed framework beats all the advanced competitors on 3 widely-used datasets. |
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)
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| Challenge: | Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. |
| Approach: | They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. |
| Outcome: | The proposed framework outperforms baseline methods in terms of effectiveness and interpretability. |
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026.findings-acl)
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| Challenge: | Long-context capabilities are essential for document and video understanding, in-contact learning, and inference-time scaling. |
| Approach: | They propose an efficient training recipe for building ultra-long context LLMs from aligned instruct model, pushing the boundaries of context lengths from 128K to 1M, 2M, and 4M tokens. |
| Outcome: | The proposed model extends the context window while maintaining short context capabilities while maintaining the performance of the existing model. |
Bridging the Sensory Gap: Visual Injection for Taxonomy Completion (2026.acl-long)
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| Challenge: | Existing text-only methods suffer from a "Sensory Gap" in integrating new concepts into existing hierarchies. |
| Approach: | They propose a framework leveraging Visual Injection for Taxonomy Completion that maps synthesized images into intrinsic pseudo-tokens and decouples magnitude from selection to prevent visual signals from being drowned out. |
| Outcome: | Experiments on three datasets show that VITC achieves state-of-the-art performance . it delivers an average absolute gain of over 19% in Hit@1. |
LCMA-SRT: Language-Conditional Mixture-of-Experts Adapters for Joint Multilingual Speech Recognition and Translation (2026.acl-long)
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| Challenge: | Existing hierarchical transducers suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive. |
| Approach: | They propose a hierarchical transducer with language-conditional Mixture-of-Experts adapters to improve multilingual joint automatic speech recognition and speech translation. |
| Outcome: | Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines. |
ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding (2026.findings-acl)
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| Challenge: | Existing omni-multimodal large language models lack incomplete modality support or lack autonomous proactive monitoring. |
| Approach: | They propose a real-time omni-multimodal assistant for unified reactive and proactive interaction that decouples response initiation from generation to ensure precise triggering without task conflict. |
| Outcome: | The proposed model achieves state-of-the-art performance on proactive tasks while competing in reactive settings. |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
| Challenge: | a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy. |
| Approach: | They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition . |
| Outcome: | The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks. |
CNEQ: Incorporating numbers into Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Complex query answering (CQA) is a task that addresses semantics of numerical entities. |
| Approach: | They propose a model that includes a Number-Entity Predictor and an Entity Filter . they use three widely-used Knowledge Graphs to perform reasoning over knowledge graphs . |
| Outcome: | The proposed model can predict entities and numerical values better than existing models . it compares or filters out entities that meet certain constraints on three widely-used Knowledge Graphs . |
Semi-Supervised Disfluency Detection (C18-1)
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| Challenge: | Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure. |
| Approach: | They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN). |
| Outcome: | The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality . |
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection (2024.findings-acl)
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| Challenge: | Existing studies on ideology detection focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies. |
| Approach: | They propose a concept semantics-enhanced framework for multifaceted ideology detection . it enables concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics. |
| Outcome: | The proposed framework achieves state-of-the-art in the cross-topic scenario and on the benchmark dataset. |
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)
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Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng
| Challenge: | Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome . |
| Approach: | They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens. |
| Outcome: | The proposed method reduces token usage by up to 44% while preserving accuracy. |
Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation (2025.coling-main)
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| Challenge: | Current named entity recognition methods struggle with text-image mismatch problem due to a lack of visual context. |
| Approach: | They propose an adaptive mixup image augmentation method that generates augmented images based on matching score between text and image . |
| Outcome: | The proposed method can be integrated into existing models and demonstrate consistent performance improvements. |
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
| Approach: | They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs. |
| Outcome: | The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought. |
Scheduled DropHead: A Regularization Method for Transformer Models (2020.findings-emnlp)
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| Challenge: | DropHead is a structured dropout method for regularizing multi-head attention . DropHed drops entire attention heads during training to prevent overfitting . |
| Approach: | They propose a structured dropout method specifically designed for regularizing multi-head attention mechanism . DropHead drops entire attention heads during training to prevent overfitting . |
| Outcome: | The proposed method can improve transformer models by 0.9 BLEU score on translation task and around 1.0 accuracy for various text classification tasks. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |
Evian: Towards Explainable Visual Instruction-tuning Data Auditing (2026.findings-acl)
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| Challenge: | Existing data filtering methods rely on coarse-grained scores that lack granularity to identify nuanced semantic flaws. |
| Approach: | They propose a "Decomposition-then-Evaluation" paradigm that breaks model responses into constituent cognitive components. |
| Outcome: | The proposed model outperforms models trained on larger datasets in three key areas . the authors show that Logical Coherence is the most critical factor in data quality evaluation . |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
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| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024.acl-long)
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Sebastian Joseph, Lily Chen, Jan Trienes, Hannah Göke, Monika Coers, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | FactPICO is a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials . existing metrics for factual summarizing medical evidence are poorly correlated with expert judgments on the instance level. |
| Approach: | They propose a factuality benchmark for plain language summarization of medical texts . they assess factuality of critical elements of RCTs in those summaries . |
| Outcome: | The proposed benchmark assesses the factuality of medical summaries using LLMs . the summary summators are based on 345 plain language summaires with fine-grained evaluation . |
Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)
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| Challenge: | Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation. |
| Approach: | They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback. |
| Outcome: | The proposed method achieves state-of-the-art on seven semantic text similarity tasks. |
CARE: Multilingual Human Preference Learning for Cultural Awareness (2025.emnlp-main)
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| Challenge: | Language Models are tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. |
| Approach: | They introduce a multilingual resource that contains culturally specific questions and 31.7k responses with human judgments. |
| Outcome: | The proposed model outperforms models with stronger initial cultural performance . the proposed model has gaps in the literature on culturally relevant data . |
MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in machine learning (MU) have enabled the selective removal of private or sensitive information encoded within deep neural networks. |
| Approach: | They propose to "reformulate" the task of multimodal MU in the era of MLLMs by preserving only the visual patterns associated with a given entity while preserving the corresponding textual knowledge. |
| Outcome: | The proposed method surpasses baselines that finetuned MLLMs with VQA data directly through Gradient Ascent (GA) or Negative Preference Optimization (NPO), across all evaluation dimensions. |
AFPQ: Asymmetric Floating Point Quantization for LLMs (2024.findings-acl)
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| Challenge: | Low-bit weight quantization can save memory and accelerate inference. |
| Approach: | They propose asymmetric FP quantization which sets separate scales for positive and negative values. |
| Outcome: | The proposed method leads to large accuracy improvements and can be easily plugged into other quantization methods, including GPTQ and AWQ, for better performance. |
Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation (2023.findings-eacl)
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| Challenge: | Existing methods focusing on this task usually concatenate the concatened concepts words as the inputs of a pre-trained language model (PLM) however, in pre-training, the input is often corrupted sentences with correct word order. |
| Approach: | They propose a two-stage framework to improve the ability of pre-trained language models to deal with masked sentences with incorrect word order and a special token to make the input distribution more similar to the one used in pre-training. |
| Outcome: | The proposed method is able to generate a sentence containing all given concepts and correctly describe the relations between concepts. |
DistillCSE: Distilled Contrastive Learning for Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing approaches to sentence embeddings are based on contrastive learning (CL) . |
| Approach: | They propose a framework which performs contrastive learning under the self-training paradigm with knowledge distillation and propose 'Group-P shuffling strategy' and averaging logits from multiple teacher components. |
| Outcome: | The proposed framework outperforms many strong baseline methods and yields a new state-of-the-art performance. |
What are Foundation Models Cooking in the Post-Soviet World? (2025.emnlp-main)
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| Challenge: | During the Soviet era, these identities were pressured through forced assimilation under the Russian language and culture. |
| Approach: | They construct a multi-modal dataset encompassing 1147 and 823 dishes in the Russian and Ukrainian languages, centered around the Post-Soviet region. |
| Outcome: | The results show that leading models struggle to correctly identify the origins of dishes from Post-Soviet nations in both text-only and multi-modal Question Answering (QA) the weak correlation between this task and QA suggests that QA alone may be insufficient as an evaluation of cultural understanding. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)
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| Challenge: | Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules. |
| Approach: | They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network. |
| Outcome: | The proposed method outperforms existing models on three human-annotated datasets. |
LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)
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| Challenge: | Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach. |
| Approach: | They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner . |
| Outcome: | The proposed model correlates much better with human judgment than existing metrics. |
Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification (2023.findings-acl)
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| Challenge: | Existing strategies to teach pre-trained models to generate simple texts are inadequate. |
| Approach: | They propose a continued pre-training strategy to teach pre-trained models to generate simple texts by randomly masking text spans in ordinary texts. |
| Outcome: | The proposed strategy improves on lexical simplification, sentence simplification and document-level simplification tasks over existing models. |
Learning to Sample Replacements for ELECTRA Pre-Training (2021.findings-acl)
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| Challenge: | Experimental results show that ELECTRA pretrains a discriminator to detect replaced tokens . despite compelling performance, there is no direct feedback loop from discriminator and generator to generator, making replacements biased to correct tokens. |
| Approach: | They propose to augment sampling with a hardness prediction mechanism to encourage the discriminator to learn what it has not acquired. |
| Outcome: | The proposed method improves ELECTRA pre-training on various downstream tasks. |
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)
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Jingcheng Deng, Zhongtao Jiang, Liang Pang, Zihao Wei, Liwei Chen, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng
| Challenge: | Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data. |
| Approach: | They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment. |
| Outcome: | The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data. |
FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for updating large language models are inefficient in multi-client scenarios . Existing approaches assume a single-user setting and are ineffective in multiclient scenarios. |
| Approach: | They propose a new task that enables multiple clients to perform LEKE while preserving privacy and reducing computational overhead. |
| Outcome: | The proposed framework outperforms existing LEKE frameworks on two benchmark datasets and retains 96% of performance. |
mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)
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Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang
| Challenge: | Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes. |
| Approach: | They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis. |
| Outcome: | The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow. |
Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)
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| Challenge: | Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span. |
| Approach: | They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features. |
| Outcome: | The proposed model extracts aspect-specific opinion spans and evaluates sentiment polarity using extracted opinion features. |
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process . |
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
| Outcome: | The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%. |
Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning (2026.acl-long)
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| Challenge: | Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. |
| Approach: | They propose a method to use both positive and negative distilled reasoning traces to maximize LLM reasoning performance in offline settings. |
| Outcome: | The proposed model outperforms existing methods in the distillation context. |
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer (2024.acl-long)
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| Challenge: | Existing methods for short TST are difficult to implement and can cause content degradation. |
| Approach: | They propose a method to vary the style polarity of text while preserving semantic content. |
| Outcome: | The proposed method improves over baselines and is highly efficient. |
GUI0: Self-Evolving Foundational GUI Agents in Super App Ecosystems (2026.acl-long)
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Xinyi Wang, Wei Dai, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, null Kangqin, Zhongpu Wang, Xiaode Zhang, Yanming Liu, Jihao Gu, Jingtao Xu, Gong Zhi
| Challenge: | Automated interaction with graphical user interfaces (GUIs) is central to general artificial intelligence, but remains challenging within Super App ecosystems. |
| Approach: | They propose a framework synergizing autonomous data synthesis with dual-agent co-evolution . GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning . |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro and Claude-4-Sonnet in the SuperAPP benchmark and has universal efficacy across base models. |
Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)
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| Challenge: | Existing knowledge graphs that represent entities in different languages are not covered by existing systems. |
| Approach: | They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other. |
| Outcome: | The proposed method significantly outperforms existing systems on two benchmark datasets. |
Visualizing and Understanding the Effectiveness of BERT (D19-1)
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| Challenge: | Language model pre-training, such as BERT, has achieved strong performance in many NLP tasks. |
| Approach: | They propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets. |
| Outcome: | The proposed model improves performance and generalization capability across tasks. |
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation (2023.emnlp-demo)
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| Challenge: | Existing tools for fine-grained human evaluation lack adaptability to different domains or languages, or modify annotation settings according to user needs. |
| Approach: | They propose a unified platform for fine-grained evaluation that is customizable and deployable with a single YAML configuration file. |
| Outcome: | The proposed frameworks are based on a single YAML configuration file and can be easily extended to different domains or languages. |
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment (2024.emnlp-main)
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| Challenge: | Existing evaluation resources lack domain and language diversity, limiting the ability for cross-domain and cross-lingual analyses. |
| Approach: | They propose to use a multilingual multi-domain dataset to benchmark multilingual and monolingual models for multilingual readability assessment. |
| Outcome: | The proposed model trains better in supervised, unsupervised, and few-shot prompting settings and identifies shortcomings in state-of-the-art unsupervised methods. |
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)
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| Challenge: | Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes. |
| Approach: | They propose to use latent meanings of morphological compositions of words to train word embeddings. |
| Outcome: | The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks. |
MarkupLM: Pre-training of Text and Markup Language for Visually Rich Document Understanding (2022.acl-long)
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| Challenge: | Existing layout-based pre-training approaches are not easy to apply to VRDU tasks. |
| Approach: | They propose to use markup languages as the backbone for document understanding tasks where text and markup information are jointly pre-trained. |
| Outcome: | The proposed model outperforms existing models on document understanding tasks. |
Matching Article Pairs with Graphical Decomposition and Convolutions (P19-1)
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| Challenge: | Existing methods for matching sentence pairs do not perform well in longer documents . Existing approaches for matching sentences do not work in longer document understanding tasks . |
| Approach: | They propose to model article pairs by comparing sentences that enclose same concept vertex . they propose to use a concept interaction graph to match articles by encoding sentences . |
| Outcome: | The proposed methods show significant improvements over existing methods . the proposed datasets consist of 30K pairs of breaking news articles . |
Chinese Metaphorical Relation Extraction: Dataset and Models (2023.findings-emnlp)
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| Challenge: | Metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text. |
| Approach: | They propose a new formulation of metaphor identification as a relation extraction problem . they use a dataset to analyze metaphorical relations between two spans, a target and a source . |
| Outcome: | The proposed model can capture the properties of the target and source in Chinese sentences. |
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive. |
| Approach: | They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models. |
| Outcome: | The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models. |
Coarse-to-Fine Grounded Memory for LLM Agent Planning (2025.emnlp-main)
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| Challenge: | Existing methods to enhance LLM with offline experiences or online trajectory analysis focus on single-granularity memory derived from dynamic environmental interactions. |
| Approach: | They propose a framework that grounds coarse-to-fine memories with LLM to enable flexible adaptation to diverse scenarios. |
| Outcome: | Extensive experiments on AlfWorld, Webshop and ScienceWorld show that the proposed framework outperforms baselines and comprehensively optimizes memory-enhanced LLM Agent system. |
Multi-Grained Knowledge Distillation for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Pre-trained big models have delivered top performance in Seq2seq modeling, but their deployments in real-world applications are often hindered by excessive computations and memory demands. |
| Approach: | They propose a distillation scheme to efficiently transfer knowledge from big models to their cheaper counterparts. |
| Outcome: | The proposed scheme maximizes the assimilation of knowledge from the teacher model to the student model. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
SOAPTriage: SOAP-Guided Multi-View Clinical Text Modeling Framework for Automated ESI Prediction (2026.acl-long)
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Enming Wang, Jianlei Wang, Xueping Peng, Hongjiao Guan, Yinglong Wang, Sibo Wei, Jianbin Guo, Ruifeng Xu, Wenpeng Lu
| Challenge: | Emergency departments rely on the Emergency Severity Index (ESI) to assess patient acuity and prioritize care. |
| Approach: | They propose a SOAP-guided multi-view clinical text modeling framework for automated ESI prediction based on the SOAP paradigm . |
| Outcome: | The proposed framework outperforms prompting-based, multi-agent, and encoder-based baselines. |
UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database (2020.aacl-main)
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| Challenge: | Chinese and Japanese share many characters with similar surface morphology. |
| Approach: | They propose a Chinese-Japanese pretrained masked language model with a coarse-to-fine training approach to exploit the shared knowledge across the languages. |
| Outcome: | The proposed model is effective on mono- and cross-lingual Chinese and Japanese tasks. |
Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)
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| Challenge: | Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects. |
| Approach: | They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap. |
| Outcome: | The proposed framework outperforms existing methods that generate SQL queries directly. |
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)
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| Challenge: | Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories. |
| Approach: | They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution. |
| Outcome: | The proposed framework outperforms baseline methods on three language generation tasks on seven datasets. |
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text (L18-1)
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| Challenge: | ellipsis is a common linguistic phenomenon that some words are left out as they are understood from the context, especially in oral utterance. |
| Approach: | They propose to use a Chinese dependency treebank to facilitate the parsing of web text . they propose to restore omissions and reserve contexts in the web text to improve dependency parsers . |
| Outcome: | The proposed framework enables the parsing of web text from online microblogs. |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
| Approach: | They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods . |
| Outcome: | The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets. |
Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents (2025.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models (LLMs)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications. |
| Approach: | They propose a benchmark for evaluating character thought generation using literature . they propose 'MIRROR' which generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations. |
| Outcome: | The proposed benchmark outperforms existing methods in evaluating character thought generation. |
A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting (2023.findings-emnlp)
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| Challenge: | Existing work on interpreting the high-level strategic intent of humans has focused on enabling them to specify their strategy to an AI system. |
| Approach: | They propose to translate unstructured language strategies into actionable intent in the form of goals and constraints by using a game environment to build a computational interface. |
| Outcome: | The proposed model significantly outperforms human interpreters in inferring strategic intent from language (p 0.05) and in a low-data setting. |
TMATH A Dataset for Evaluating Large Language Models in Generating Educational Hints for Math Word Problems (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly being applied in education, showing significant potential in personalized instruction, student feedback, and intelligent tutoring systems (ITSs). |
| Approach: | They propose a dataset specifically designed to evaluate LLMs’ ability to generate high-quality hints for Math Word Problems. |
| Outcome: | The proposed dataset shows that LLMs can generate more accurate and contextually appropriate educational hints for math word problems without offering direct answers. |
MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been widely used in medicine but are limited in their ability to fully address the complexities of the real world. |
| Approach: | They propose a universal agent architecture for Large Language Models that integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. |
| Outcome: | The proposed framework improves the accuracy and performance of medical calculators in complex medical scenarios. |
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021.naacl-main)
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| Challenge: | a few-shot text classification task requires a large number of output classes, with few training examples per class. |
| Approach: | They propose a data augmentation technique suitable for training with limited data for few-shot, highly-multiclass text classification scenarios. |
| Outcome: | The proposed technique improves performance on four classification tasks by 3.0% on average. |
Frustratingly Easy Label Projection for Cross-lingual Transfer (2023.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer performance are based on word alignment, but no empirical studies have evaluated their effectiveness or limitations. |
| Approach: | They propose a mark-then-translate method that integrates translation and projection by inserting special markers around the labeled spans in the original sentence. |
| Outcome: | The proposed method outperforms word alignment-based methods in 57 languages and three tasks. |
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
Multi-step Jailbreaking Privacy Attacks on ChatGPT (2023.findings-emnlp)
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| Challenge: | With the rapid evolution of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. |
| Approach: | They propose to integrate ChatGPT and Bing GPT3 into their applications to create a set of LLMs that can be used to generate NLP tasks with appropriate prompts. |
| Outcome: | The proposed models can be zero-shot or few-shot learners to solve specified tasks and can even be zero or few shot learners. |
TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)
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| Challenge: | Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models . |
| Approach: | They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference. |
| Outcome: | The proposed model can speed up training and inference by 40% over previous models. |
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Improving Minimum Bayes Risk Decoding with Multi-Prompt (2024.emnlp-main)
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| Challenge: | Existing methods to generate LLMs with a single ‘best’ prompt are unstable and sub-optimal in practice. |
| Approach: | They propose to decode multiple candidate generations from a prompt bank at inference-time and use Minimum Bayes Risk (MBR) to select a final output. |
| Outcome: | The proposed method improves MBR across a set of conditional generation tasks and models. |
MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)
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| Challenge: | Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings. |
| Approach: | They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment. |
| Outcome: | The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction (2025.findings-acl)
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Yuxin Jiang, Yufei Wang, Chuhan Wu, Xinyi Dai, Yan Xu, Weinan Gan, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Wei Wang
| Challenge: | Existing methods for generating and curating high-quality instruction-tuning data rely heavily on the quality of seed data or strong assumptions about the structure and content of web documents. |
| Approach: | They propose a fully automated framework for synthesizing high-quality instruction-tuning (IT) data directly from raw web documents with minimal assumptions. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines by 16.65% across four instruction-following benchmarks. |
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)
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| Challenge: | Existing work exploits easily accessible co-occurrence information of events to learn event representations. |
| Approach: | They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning. |
| Outcome: | The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks. |
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network (P18-2)
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| Challenge: | Existing methods to rate academic papers require a lot of feature engineering and can cause inequality. |
| Approach: | They propose to use a novel convolutional neural network to automatically rate academic papers . they propose to build a dataset to automatically determine whether to accept academic papers. |
| Outcome: | The proposed model outperforms baselines by a large margin. |
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity (D19-1)
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| Challenge: | Existing methods to verify rumors are needed to identify false rumors. |
| Approach: | They propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter that exploits the temporal dynamics of stance evolution. |
| Outcome: | The proposed framework outperforms previous methods on two benchmark datasets showing that it can predict rumor stance and veracity. |
Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)
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| Challenge: | Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues. |
| Approach: | They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset. |
| Outcome: | The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
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| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |
PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning (2025.acl-long)
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Zhicong Lu, Changyuan Tian, PeiguangLi PeiguangLi, Li Jin, Sirui Wang, Wei Jia, Ying Shen, Guangluan Xu
| Challenge: | Existing studies on Large Language Models (LLMs) have failed to evaluate their performance in event reasoning with a single event relational type or reasoning format. |
| Approach: | They propose a benchmark to evaluate LLMs' event reasoning capability using a single event relational type or reasoning format. |
| Outcome: | The proposed model improves on 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity. |
Reducing Privacy Risks in Online Self-Disclosures with Language Models (2024.acl-long)
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| Challenge: | Disclosure is a social media activity that can be rewarding but also poses privacy risks. |
| Approach: | They propose to detect and abstract online self-disclosures using a large corpus of 4.8K annotated disclosure spans and a language model to fine-tune for detection. |
| Outcome: | The proposed model can detect and abstract self-disclosures with 80% accuracy, on-par with GPT-3.5. |
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)
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| Challenge: | Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features. |
| Approach: | They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities. |
| Outcome: | The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. |
On Synthetic Data for Back Translation (2022.naacl-main)
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| Challenge: | Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance. |
| Approach: | They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields . |
| Outcome: | The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall (2024.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a promising approach to address limitations of fixed knowledge in large language models. |
| Approach: | They propose a benchmark and a metric to assess LLMs' ability to generate long-form responses that exploit retrieved information. |
| Outcome: | The proposed benchmarks lack a comprehensive evaluation method to assess LLMs' ability to generate long-form responses that effectively exploits retrieved information. |
ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)
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| Challenge: | Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content . |
| Approach: | They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus. |
| Outcome: | The proposed task is feasible and challenging, and the results are beneficial for future studies. |
Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design (2026.eacl-long)
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| Challenge: | Existing AI-assisted educational tools focus on isolated tasks, but lack end-to-end workflows for instructional design. |
| Approach: | They propose a multi-agent large language model framework to automate end-to-end course material generation. |
| Outcome: | The proposed framework reduces development time and human workload while reducing human involvement. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models (2023.emnlp-main)
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| Challenge: | Existing pruning strategies struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. |
| Approach: | They propose a pruning strategy that replicates embedding space and feature space of dense language models and aims to conserve more pre-trained knowledge during the pruning process. |
| Outcome: | The proposed pruning strategy replicates embedding space and feature space of dense language models, aiming to conserve more pre-trained knowledge during the pruning process. |
Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)
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| Challenge: | Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL. |
| Approach: | They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever . |
| Outcome: | The proposed method improves embedding-based retriever and reduces cost. |
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied. |
| Approach: | They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning. |
| Outcome: | The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales. |
DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation (2025.findings-acl)
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| Challenge: | Existing code benchmarks for large language models remain static, resulting in data contamination and unreliable evaluation results. |
| Approach: | They propose a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets and provides a memorization-advantaged benchmark. |
| Outcome: | DynaCode generates 189 million unique nested code problems across 4 units of code complexity and 16 types of call graphs. |
The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education (2024.naacl-long)
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| Challenge: | Existing methods to assess instruction quality require trained raters to observe classrooms based on established criteria. |
| Approach: | They propose to use Natural Language Processing techniques to assess multiple high-inference instructional practices in in-person K-12 classrooms and simulated performance tasks for pre-service teachers. |
| Outcome: | The proposed method is able to assess multiple high-inference instructional practices in two educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. |
GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation (2024.emnlp-main)
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| Challenge: | Using large language models to jailbreak is important for testing safety and security issues. |
| Approach: | They propose an approach that leverages the reflective capabilities of large language models for jailbreaking with only black-box access. |
| Outcome: | The proposed method achieves jailbreak success rates of 98% on GPT-4, 92% on GTP-4 Turbo, and 94% on Llama-3.1-70B in under 7 queries. |
Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)
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| Challenge: | Existing corpora focus on misinformation spreading within western countries. |
| Approach: | They present a new corpus of tweets annotated with stance towards 250 misinformation claims. |
| Outcome: | The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning. |
AdapThink: Adaptive Thinking Preferences for Reasoning Language Models (2026.findings-acl)
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| Challenge: | Recent research has highlighted a significant inefficiency associated with the slow thinking paradigm . models often overthink simple tasks while underthinking complex challenges . |
| Approach: | They propose a framework for adaptive reasoning preference control that dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. |
| Outcome: | The proposed framework reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding. |
| Approach: | They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel. |
| Outcome: | The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets. |
FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)
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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy. |
| Approach: | They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem. |
| Outcome: | The proposed method matches SOTA performance while being 93.6% faster. |