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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Investigating methods to Detect Off-Topic Essays 34th

Investigating Methods to Detect Off-Topic Essays

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34th Brazilian conference on Intelligent Systems

作者： Silva, Joyce M. Anchieta, Rafael T. de Sousa, Rogerio F. Moura, Raimundo S. Fed Inst Piaui Lab Artificial Intelligence Robot & Automat Picos PI Brazil Univ Fed Piaui Lab Nat Language Proc Teresina PI Brazil

ISBN: (纸本)9783031790348;9783031790355

Automated Essay Scoring is one of the most important educational applications of natural language processing. It helps teachers with automatic assessments, providing a cheaper, faster, and more deterministic approach than humans when scoring essays. Nevertheless, off-topic essays pose challenges in this area, causing an automated grader to overestimate the score of an essay that does not adhere to a proposed topic. Thus, detecting off-topic essays is important for dealing with unrelated text responses to a given topic. This paper explored approaches based on handcrafted features to feed supervised machine-learning algorithms, tuning a BERT model, and prompt engineering with a large language model. We assessed these strategies in a public corpus of Portuguese essays, achieving the best result using a fine-tuned BERT model with a 75% balanced accuracy. Furthermore, this strategy was able to identify low-quality essays.

关键词： Automated Essay Scoring Off-Topic Essays BERT

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Reasoning-Oriented and Analogy-Based methods for Locating and Editing in Zero-Shot Event-Relational Reasoning 31

Reasoning-Oriented and Analogy-Based Methods for Locating an...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Tang, Jingyao Li, Lishuang Mi, Liteng Wu, Haiming Lu, Hongbin School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Technology Beijing Institute of Technology China School of Computer Science and Artificial Intelligence Liaoning Normal University China

ISBN: (纸本)9798891761964

Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionally, learning various relational and inferential knowledge inefficiently exploits the connections between tasks. Therefore, we first propose a method for Reasoning-Oriented Locating and Editing (ROLE), which locates and edits the key modules of the language model for reasoning about event relations, enhancing interpretability and also resource-efficiently optimizing the reasoning ability. Subsequently, we propose a method for Analogy-Based Locating and Editing (ABLE), which efficiently exploits the similarities and differences between tasks to optimize the zero-shot reasoning capability. Experimental results show that ROLE improves interpretability and reasoning performance with reduced computational cost. ABLE achieves SOTA results in zero-shot reasoning. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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empirical Evaluation of Pre-trained language Models for Summarizing Moroccan Darija News Articles 4

Empirical Evaluation of Pre-trained Language Models for Summ...

引用

4th Workshop on Arabic Corpus Linguistics, WACL 2025

作者： Aftiss, Azzedine Lamsiyah, Salima Schommer, Christoph El Alaoui, Said Ouatik Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science FSTM University of Luxembourg Esch-sur-Alzette Luxembourg

ISBN: (纸本)9798891762206

Moroccan Dialect (MD), or "Darija," is a primary spoken variant of Arabic in Morocco, yet remains underrepresented in natural language processing (NLP) research, particularly in tasks like summarization. Despite a growing volume of MD textual data online, there is a lack of robust resources and NLP models tailored to handle the unique linguistic challenges posed by MD. In response, we introduce ***_v2, an expanded version of the *** dataset, containing over 50k articles with their titles across 11 categories. This dataset provides a more comprehensive resource for developing summarization models. We evaluate the application of large language models (LLMs) for MD summarization, utilizing both fine-tuning and zero-shot prompting with encoder-decoder and causal LLMs, respectively. Our findings demonstrate that an expanded dataset improves summarization performance and highlights the capabilities of recent LLMs in handling MD text. We open-source our dataset, fine-tuned models, and all experimental code, establishing a foundation for future advancements in MD NLP. We release the code at https://***/AzzedineAftiss/Moroccan-Dialect-Summarization. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining 16th

WIBA: What Is Being Argued? A Comprehensive Approach to Argu...

引用

16th International conference on Social Networks Analysis and Mining

作者： Irani, Arman Park, Ju Yeon Esterling, Kevin Faloutsos, Michalis Univ Calif Riverside Riverside CA 92521 USA Ohio State Univ Columbus OH USA

ISBN: (纸本)9783031785405;9783031785412

How can we effectively model arguments communicated in diverse environments? On the one hand, there is a great opportunity with the abundance of digitized speech across different contexts including online forums, official proceedings, or transcripts of spoken debates. On the other hand, there is a great challenge in correctly detecting arguments, especially since each medium has its own set of conventions, lingo, affordances, and styles of argumentative engagement. We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of " What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects: (a) the existence, (b) the topic, and (c) the stance of an argument, correctly accounting for the logical dependence among the three tasks. Our algorithm leverages the fine-tuning and prompt-engineering of Large language Models. We evaluate our approach and show that it performs well in all the three capabilities. First, we develop and release an Argument Detection model that can classify a piece of text as an argument with an F-1 score between 79% and 86% on three different benchmark datasets. Second, we release a language model that can identify the topic being argued in a sentence, be it implicit or explicit, with an average similarity score of 71%, outperforming current naive methods by nearly 40%. Finally, we develop a method for Argument Stance Classification, and evaluate the capability of our approach, showing it achieves a classification F-1 score between 71% and 78% across three diverse benchmark datasets. Our evaluation demonstrates that WIBA allows the comprehensive understanding of What Is Being Argued in large corpora across diverse contexts, which is of core interest to many applications in linguistics, communication, and social and computer science. To facilitate accessibility to the advancements outlined in this work, we release WIBA as a free open access platform (***) and API.

关键词： Argument Mining Large language Models natural language processing

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Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large language Models 31

Breaking the Stage Barrier: A Novel Single-Stage Approach to...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Lian, Haoran Chen, Junmin Huang, Wei Xiong, Yizhe Hu, Wenping Ding, Guiguang Chen, Hui Niu, Jianwei Lin, Zijia Zhang, Fuzheng Zhang, Di Beihang University China Kuaishou Technology China Tsinghua University China BUPT China Zhongguancun Laboratory China Zhengzhou University China

ISBN: (纸本)9798891761964

Recently, Large language models (LLMs) have revolutionized natural language processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long token sequences, limiting their performance on various downstream tasks. Current solutions toward long context modeling often employ multi-stage continual pertaining, which progressively increases the effective context length through several continual pretraining stages. However, those approaches require extensive manual tuning and human expertise. In this paper, we introduce a novel single-stage continual pretraining method, Head-Adaptive Rotary Position Encoding (HARPE), to equip LLMs with long context modeling capabilities while simplifying the training process. Our HARPE leverages different Rotary Position Encoding (RoPE) base frequency values across different attention heads and directly trains LLMs on the target context length. Extensive experiments on 4 language modeling benchmarks, including the latest RULER benchmark, demonstrate that HARPE excels in understanding and integrating long-context tasks with single-stage training, matching and even outperforming existing multi-stage methods. Our results highlight that HARPE successfully breaks the stage barrier for training LLMs with long context modeling capabilities. © 2025 Association for Computational Linguistics.

关键词： Encoding (symbols)

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Word-level Cross-lingual Structure in Large language Models 31

Word-level Cross-lingual Structure in Large Language Models

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Feng, Zihao Cao, Hailong Xu, Wang Zhao, Tiejun Faculty of Computing Harbin Institute of Technology China

ISBN: (纸本)9798891761964

Large language Models (LLMs) have demonstrated exceptional performance across a broad spectrum of cross-lingual natural language processing (NLP) tasks. However, previous methods predominantly focus on leveraging parallel corpus to conduct instruction data for continuing pre-training or fine-tuning. They ignored the state of parallel data on the hidden layers of LLMs. In this paper, we demonstrate Word-level Cross-lingual Structure (WCS) of LLM which proves that the word-level embedding on the hidden layers are isomorphic between languages. We find that the hidden states of different languages' input on the LLMs hidden layers can be aligned with an orthogonal matrix on word-level. We prove this conclusion in both mathematical and downstream task ways on two representative LLM foundations, LLaMA2 and BLOOM. Besides, we propose an Isomorphism-based Data Augmentation (IDA) method to apply the WCS on a downstream cross-lingual task, Bilingual Lexicon Induction (BLI), in both supervised and unsupervised ways. The experiment shows the significant improvement of our proposed method over all the baselines, especially on low-resource languages. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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LLM-Driven Chest X-Ray Report Generation With a Modular, Reduced-Size Architecture 34th

LLM-Driven Chest X-Ray Report Generation With a Modular, Red...

引用

34th Brazilian conference on Intelligent Systems

作者： Vargas, Talles Viana Pedrini, Helio Santanche, Andre Univ Estadual Campinas Inst Comp IC Campinas SP Brazil

ISBN: (纸本)9783031790317;9783031790324

Large language Models (LLMs) have been widely employed in various text processing tasks. In computer vision, these models have found application in generating captions and text from natural images, as well as in Visual Question Answering (VQA) systems. In the field of medical imaging, there are studies based on text generation proposing automated diagnoses of X-rays, magnetic resonance imaging scans, computed tomography scans, and other modalities. Few initiatives seek to apply and harness the potential of LLMs in medical text generation;they use models with tens of billions of parameters and are thus computationally expensive. This work addresses this gap by evaluating the use of frozen pre-trained models (CXAS U-Net and BioGPT) for chest Xray report generation. We adapt the BLIP-2 modular architecture where only a cross-modal alignment module must be trained in order to generate text from images. We were able to achieve competitive scores over Clinical Efficacy (CE) metrics compared to some state-of-the-art (SOTA) methods, while obtaining lower scores for natural language Generation (NLG) metrics. Our findings suggest that NLG metrics may not serve as suitable proxies for evaluating models in the chest X-ray generation task.

关键词： Radiology Report Generation language Models Lightweight Training Chest X-Rays

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Prompt-augmented Feature with Cross-domain Contrastive Learning for Efficient Multi-domain Sentiment Analysis

Prompt-augmented Feature with Cross-domain Contrastive Learn...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Rui Yang, Tao Liu, Cheng Jiang, Dazhi Wong, Hau-San Wu, Si Department of Computer Science Shantou University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Engineering South China University of Technology China

ISBN: (纸本)9798350368741

Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component. © 2025 IEEE.

关键词： multi-domain learning pre-trained language model prompt augmentation Sentiment analysis sentiment-aware feature

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Federated Learning and Parallel Prompt Scheduling Strategies for Large language Models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated Learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. The utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-PPS’ to explore a novel approach for large language models within federated learning. This method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various natural language processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Inference Retrieval-Augmented Multi-Modal Chain-of-Thoughts Reasoning for language Models

Inference Retrieval-Augmented Multi-Modal Chain-of-Thoughts ...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： He, Qiangqiang Qian, Shuwei Zhang, Jie Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798350368741

Recent advancements in Large language Models (LLMs) have catalyzed the exploration of Chain of Thought (CoT) approaches, particularly in extending their application to multimodal tasks to enhance reasoning capabilities. However, current studies often fail to fully leverage the inferential capabilities of these models, as they primarily focus on selecting similar questions or images rather than exploring identical inferences, thereby limiting the potential for analogical learning. In this paper, we propose an inference retrieval-augmented method that incorporates two strategies to identify training set examples with inferential processes similar to those of the target problem. Initially, we fine-tune a vanilla model to generate the pseudo-inference relevant to the question, which is then utilized to retrieve analogous examples. Next, we hypothesize that questions sharing similar contexts may share inferences, thus retrieving examples based on similarity in contextual questions. Ultimately, we construct multiple reasoning pathways from the retrieved examples and employ a voting mechanism to determine the most frequent answer. Our method surpasses all few-shot approaches and most supervised methods on the ScienceQA dataset, achieving an accuracy of 87.37% with ChatGPT and exceeding the human benchmark in several categories. © 2025 IEEE.

关键词： Chain of Thought Large language Model Multimodal Reasoning

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