搜索结果：Findings of ACL. ACL

Despite its success in self-supervised learning, contrastive learning is less studied in the supervised setting. In this work, we first use a set of pilot experiments to show that in the supervised setting, the cross-entropy loss objective (CE) and the contrastive learning objective often conflict with each other, thus hindering the applications of CL in supervised settings. To resolve this problem, we introduce a novel \underline{A}ligned \underline{C}ontrastive \underline{L}earning (ACL) framework. First, ACL-Embed regards label embeddings as extra augmented samples with different labels and employs contrastive learning to align the label embeddings with its samples' representations. Second, to facilitate the optimization of ACL-Embed objective combined with the CE loss, we propose ACL-Grad, which will discard the ACL-Embed term if the two objectives are in conflict. To further enhance the performances of intermediate exits of multi-exit BERT, we further propose cross-layer ACL (ACL-CL), which is to ask the teacher exit to guide the optimization of student shallow exits. Extensive experiments on the GLUE benchmark results in the following takeaways: (a) ACL-BRT outperforms or per

Recently, we have often observed hallucinated citations or references that do not correspond to any existing work in papers under review, preprints, or published papers. Such hallucinated citations pose a serious concern to scientific reliability. When they appear in accepted papers, they may also negatively affect the credibility of conferences. In this study, we refer to hallucinated citations as "HalluCitation" and systematically investigate their prevalence and impact. We analyze all papers published at ACL, NAACL, and EMNLP in 2024 and 2025, including main conference, Findings, and workshop papers. Our analysis reveals that nearly 300 papers contain at least one HalluCitation, most of which were published in 2025. Notably, half of these papers were identified at EMNLP 2025, the most recent conference, indicating that this issue is rapidly increasing. Moreover, more than 100 such papers were accepted as main conference and Findings papers at EMNLP 2025, affecting the credibility.

Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10 to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multiscale formulation for Data Driven Prognosis (DDP) of post ACLR is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. The proposed DDP scheme is capable of predicting onset of instabilities. Since the need for off line testing (or training) is obviated, it can be easily implemented for ACLR, where such controlled a priori testing is almost impossible to conduct. The DDP algorithm facilitates hierarchical handling of the large data set, and can assess the state of recovery in post ACLR conditions based on data collected from stair ascent and descent exercises of subjects. The DDP algorithm identifies inefficient knee varus motion and knee rotation as primary difficulties experienced

Modern computer vision models have proven to be highly useful for medical imaging classification and segmentation tasks, but the scarcity of medical imaging data often limits the efficacy of models trained from scratch. Transfer learning has emerged as a pivotal solution to this, enabling the fine-tuning of high-performance models on small data. Mei et al. (2022) found that pre-training CNNs on a large dataset of radiologist-labeled images (RadImageNet) enhanced model performance on downstream tasks compared to ImageNet pretraining. The present work extends Mei et al. (2022) by conducting a comprehensive investigation to determine optimal CNN architectures for breast lesion malignancy detection and ACL tear detection, as well as performing statistical analysis to compare the effect of RadImageNet and ImageNet pre-training on downstream model performance. Our findings suggest that 1-dimensional convolutional classifiers with skip connections, ResNet50 pre-trained backbones, and partial backbone unfreezing yields optimal downstream medical classification performance. Our best models achieve AUCs of 0.9969 for ACL tear detection and 0.9641 for breast nodule malignancy detection, compe

The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP). This paper examines the literature as a whole to identify broad trends in productivity, focus, and impact. It presents the analyses in a sequence of questions and answers. The goal is to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations. Special emphasis is laid on the demographics and inclusiveness of NLP publishing. Notably, we find that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience. We hope that recording citation and participation gaps across demographic groups will encourage more inclusiveness and fairness in research.

The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics (CL). This paper presents a tool called ``ACL Anthology Helper''. It automates the process of parsing and downloading papers along with their meta-information, which are then stored in a local MySQL database. This allows for efficient management of the local papers using a wide range of operations, including "where," "group," "order," and more. By providing over 20 operations, this tool significantly enhances the retrieval of literature based on specific conditions. Notably, this tool has been successfully utilised in writing a survey paper (Tang et al.,2022a). By introducing the ACL Anthology Helper, we aim to enhance researchers' ability to effectively access and organise literature from the ACL Anthology. This tool offers a convenient solution for researchers seeking to explore the ACL Anthology's vast collection of publications while allowing for more targeted and efficient literature retrieval.

The recent surge of language models (LMs) has rapidly expanded NLP/AI research, driving an exponential rise in submissions and acceptances at major conferences. Yet this growth has been shadowed by escalating concerns over conference quality, such as plagiarism, reviewer inexperience, and collusive bidding. However, existing studies rely largely on qualitative accounts, for example expert interviews and social media discussions, lacking longitudinal empirical evidence. To fill this gap, we conduct a ten-year empirical study (2014-2024) spanning seven leading conferences. We build a four-dimensional bibliometric framework covering conference scale, core citation statistics, impact dispersion, and cross-venue and journal influence. Notably, we further propose a metric called Quality-Quantity Elasticity (QQE), which measures the elasticity of citation growth relative to acceptance growth. We highlight two key findings. First, conference expansion does not lead to proportional growth in scholarly impact, as QQE consistently declines over time across all venues. Second, ACL has not lost its crown, continuing to outperform other NLP conferences in median citations, milestone contribution

Academic researchers need efficient and reliable methods for collecting high-quality information from trusted sources, but modern tools for AI-assisted research still suffer from the tendency of Large Language Models (LLMs) to produce factually inaccurate or nonsensical output, commonly referred to as hallucinations. We apply the extractive question answering system VerbatimRAG to research papers in the ACL Anthology, directly mapping user queries to verbatim text spans in retrieved documents. We contribute a novel ground truth dataset for the task of mapping user queries to relevant text spans in research papers, and use it to train and evaluate a variety of extractive models. Human annotation is performed by NLP researchers and is based on synthetic user queries generated using a custom pipeline based on the ScIRGen methodology, paired with chunks of research papers retrieved by VerbatimRAG. On this benchmark, a 150M-parameter ModernBERT token classifier trained on silver supervision from our pipeline achieves the best word-level F1 (53.6), ahead of the strongest evaluated LLM extractor (48.7).

Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. The prevailing methods typically learn a conservative policy to mitigate the problem of Q-value overestimation, but it is prone to overdo it, leading to an overly conservative policy. Moreover, they optimize all samples equally with fixed constraints, lacking the nuanced ability to control conservative levels in a fine-grained manner. Consequently, this limitation results in a performance decline. To address the above two challenges in a united way, we propose a framework, Adaptive Conservative Level in Q-Learning (ACL-QL), which limits the Q-values in a mild range and enables adaptive control on the conservative level over each state-action pair, i.e., lifting the Q-values more for good transitions and less for bad transitions. We theoretically analyze the conditions under which the conservative level of the learned Q-function can be limited in a mild range and how to optimize each transition adaptively. Motivated by the theoretical analysis, we propose a novel algorithm,

What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern identification process. Through a survey, we compare the ethical concerns of the corpus to the concerns listed by the general public and professionals in the field. Finally, we compare our retrieved ethical concerns with existing taxonomies pointing to gaps and future research directions.

The Anterior Cruciate Ligament rupture is a very common knee injury during sport activities. Landing after a jump is one of the most prominent human body movement scenarios that can lead to such an injury. The landing - related Anterior Cruciate Ligament (ACL) injury risk factors have been in the spotlight of research interest. Computational modeling and simulation allows for studying musculoskeletal systems. Specifically, predictive simulation approaches offer researchers the opportunity to predict and study new biological motions without the demands of acquiring experimental data. In this thesis, we present a pipeline that aims to predict and identify key parameters of interest that are related to ACL injury during single - leg landings. We examined the following conditions of single - leg landing: a) initial landing height, b) hip internal and external rotation, c) lumbar forward - backward leaning, d) lumbar medial - lateral bending, e) lumbar internal - external rotation, f) muscle forces permutations and g) effort goal weight. Identified on related research studies, we evaluated the following risk factors: vertical Ground Reaction Force, knee joint Anterior force, knee joint

We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based method that exploits a regularization term to smooth the miscalibrated confidences. Although these approaches have shown the effectiveness on calibrating the networks, there is still a lack of understanding on the underlying principles of regularization in terms of network calibration. We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration. Specifically, we have observed that 1) the regularization-based methods can be interpreted as variants of label smoothing, and 2) they do not always behave desirably. Based on the analysis, we introduce a novel loss function, dubbed ACLS, that unifies the merits of existing regularization methods, while avoiding the limitations. We show extensive experimental results for image classification and semantic segmentation on standard benchmarks, including CIFAR10, Tiny-ImageNet, ImageNet, and PASCAL VOC, demonstrating the effectiveness of our loss function.

Multi-step reasoning is essential for large language models (LLMs), yet multilingual performance remains challenging. While Chain-of-Thought (CoT) prompting improves reasoning, it struggles with non-English languages due to the entanglement of reasoning and execution. Program-of-Thought (PoT) prompting separates reasoning from execution, offering a promising alternative but shifting the challenge to generating programs from non-English questions. We propose a framework to evaluate PoT by separating multilingual reasoning from code execution to examine (i) the impact of fine-tuning on question-reasoning alignment and (ii) how reasoning quality affects answer correctness. Our findings demonstrate that PoT fine-tuning substantially enhances multilingual reasoning, outperforming CoT fine-tuned models. We further demonstrate a strong correlation between reasoning quality (measured through code quality) and answer accuracy, highlighting its potential as a test-time performance improvement heuristic.

Timely and accurate conflict event data are essential for real-time monitoring, forecasting, and policy response. Yet near-real-time conflict datasets such as the Armed Conflict Location \& Event Data Project (ACLED) are subject to reporting delays, that is, delays between event occurrence and first inclusion in the database. Such delays can introduce bias in short-term analyses and forecasts. This study provides a statistical analysis of reporting delays for African events recorded in ACLED's weekly releases from June 30, 2024, to June 1, 2025. Treating delay as a discrete time duration, we estimate grouped proportional hazards models with additive-linear and smooth terms incorporating event-level, spatial, and country-level covariates. Our results show that more than half of events are reported within two weeks, but delays vary systematically by event type, fatalities, geographic location, and political regime. Higher-fatality events are reported more quickly, while events in more restrictive political and informational environments tend to be reported more slowly. We also find substantial between-country heterogeneity, and country-specific analyses indicate that event-level

Automated definition generation systems have been proposed to support vocabulary expansion for language learners. The main barrier to the success of these systems is that learners often struggle to understand definitions due to the presence of potentially unfamiliar words and grammar, particularly when non-standard language is involved. To address these challenges, we propose CLIX, the task of Cross-Lingual explanations of Idiomatic eXpressions. We explore the capabilities of current NLP models for this task, and observe that while it remains challenging, large language models show promise. Finally, we perform a detailed error analysis to highlight the key challenges that need to be addressed before we can reliably incorporate these systems into educational tools.

We investigate the impacts of NLP research published in top-tier conferences (i.e., ACL, EMNLP, and NAACL) from 1979 to 2024. By analyzing citations from research articles and external sources such as patents, media, and policy documents, we examine how different NLP topics are consumed both within the academic community and by the broader public. Our findings reveal that language modeling has the widest internal and external influence, while linguistic foundations have lower impacts. We also observe that internal and external impacts generally align, but topics like ethics, bias, and fairness show significant attention in policy documents with much fewer academic citations. Additionally, external domains exhibit distinct preferences, with patents focusing on practical NLP applications and media and policy documents engaging more with the societal implications of NLP models.

Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to the domain gap between the synthetic and real-world datasets or the requirement of prior information. To overcome these limitations, we propose a novel self-supervised framework ACL-SPC for point cloud completion to train and test on the same data. ACL-SPC takes a single partial input and attempts to output the complete point cloud using an adaptive closed-loop (ACL) system that enforces the output same for the variation of an input. We evaluate our proposed ACL-SPC on various datasets to prove that it can successfully learn to complete a partial point cloud as the first self-supervised scheme. Results show that our method is comparable with unsupervised methods and achieves superior performance on the real-world dataset compared to the supervised methods trained on the synthetic dataset. Extensive experiments justify the necessity of self-supervised learning and the

We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain. Integrating and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes metadata, PDF files, citation graphs and additional structured full texts with sections, figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL spans seven decades, containing 73K papers, alongside 210K figures. We spotlight how ACL OCL applies to observe trends in computational linguistics. By detecting paper topics with a supervised neural model, we note that interest in "Syntax: Tagging, Chunking and Parsing" is waning and "Natural Language Generation" is resurging. Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).

ACL configuration is essential for managing network flow reachability, yet its complexity grows significantly with topologies and pre-existing rules. To carry out ACL configuration, the operator needs to (1) understand the new configuration policies or intents and translate them into concrete ACL rules, (2) check and resolve any conflicts between the new and existing rules, and (3) deploy them across the network. Existing systems rely heavily on manual efforts for these tasks, especially for the first two, which are tedious, error-prone, and impractical to scale. We propose Xumi to tackle this problem. Leveraging LLMs with domain knowledge of the target network, Xumi automatically and accurately translates the natural language intents into complete ACL rules to reduce operators' manual efforts. Xumi then detects all potential conflicts between new and existing rules and generates resolved intents for deployment with operators' guidance, and finally identifies the best deployment plan that minimizes the rule additions while satisfying all intents. Evaluation shows that Xumi accelerates the entire configuration pipeline by over 10x compared to current practices, addresses O(100) conf