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

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A Dual-Perspective Metaphor Detection Framework Using Large language Models

A Dual-Perspective Metaphor Detection Framework Using Large ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yujie Lin Jingyao Liu Yan Gao Ante Wang Jinsong Su School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets.

关键词： Large language models Supervised learning Semantics Decision making Knowledge graphs Reliability theory Signal processing Cognition natural language processing Speech processing

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Multi-Objective Large language Model Unlearning

Multi-Objective Large Language Model Unlearning

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zibin Pan Shuwen Zhang Yuesheng Zheng Chi Li Yuheng Cheng Junhua Zhao School of Science and Engineering CUHKSZ Shenzhen China School of Data Science CUHKSZ Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Machine unlearning in the domain of large language models (LLMs) has attracted great attention recently, which aims to effectively eliminate undesirable behaviors from LLMs without full retraining from scratch. In this paper, we explore the Gradient Ascent (GA) approach in LLM unlearning, which is a proactive way to decrease the prediction probability of the model on the target data in order to remove their influence. We analyze two challenges that render the process impractical: gradient explosion and catastrophic forgetting. To address these issues, we propose Multi-Objective Large language Model Unlearning (MOLLM) algorithm. We first formulate LLM unlearning as a multi-objective optimization problem, in which the cross-entropy loss is modified to the unlearning version to overcome the gradient explosion issue. A common descent update direction is then calculated, which enables the model to forget the target data while preserving the utility of the LLM. Our empirical results verify that MoLLM outperforms the SOTA GA-based LLM unlearning methods in terms of unlearning effect and model utility preservation. The source code is available at https://***/zibinpan/MOLLM.

关键词： Large language models Source coding Signal processing algorithms Signal processing Predictive models Prediction algorithms Explosions Data models Speech processing Optimization

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DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems 25

DLCRec: A Novel Approach for Managing Diversity in LLM-Based...

引用

18th International conference on Web Search and Data Mining-WSDM

作者： Chen, Jiaju Gao, Chongming Yuan, Shuai Liu, Shuchang Cai, Qingpeng Jiang, Peng Univ Sci & Technol China Hefei Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798400713293

The integration of Large language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction. To address this issue, controllable recommendation has emerged as a promising approach, allowing users to specify their preferences and receive recommendations that meet their diverse needs. Despite its potential, existing controllable recommender systems frequently rely on simplistic mechanisms, such as a single prompt, to regulate diversity-an approach that falls short of capturing the full complexity of user preferences. In response to these limitations, we propose DLCRec, a novel framework designed to enable fine-grained control over diversity in LLM-based recommendations. Unlike traditional methods, DLCRec adopts a well-designed task decomposition strategy, breaking down the recommendation process into three sequential sub-tasks: genre prediction, genre filling, and item prediction. These sub-tasks are trained independently and inferred sequentially according to user-defined control numbers, ensuring more precise control over diversity. Furthermore, the scarcity and uneven distribution of diversity-related user behavior data pose significant challenges for fine-tuning. To overcome these obstacles, we introduce two data augmentation techniques that enhance the model's robustness to noisy and out-of-distribution data. These techniques expose the model to a broader range of patterns, improving its adaptability in generating recommendations with varying levels of diversity. Our extensive empirical evaluation demonstrates that DLCRec not only provides precise control over diversity but also outperforms state-of-the-art baselines across multiple recommendation scenarios.

关键词： Controllable Recommendation Large language Models Diversity

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Analyzing and Visualizing Urdu News Articles Using NLP and Concept Graphs

Analyzing and Visualizing Urdu News Articles Using NLP and C...

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International conference on Advancements in Computational Sciences (ICACS)

作者： Aneela Mehmood Malik Tahir Hassan Qadeer Baig School of Systems and Technology University of Management and Technology Lahore Pakistan Faculty of information technology University of Central Punjab Lahore Pakistan Founder of Newsarchives.pk Canada

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper investigates Urdu news articles using the natural language processing (NLP) concept graph generation technique. We gather news data (web scraping), do text pre-processing, translate it into Urdu, and calculate the similarity between articles by performing cosine similarities. At last, we present concept graphs (also referred to as conjunction graphs)-relationships between news articles—to visually tell the tale. Our method gives insights into the structure and connections in an empirical news corpus, pointing out significant trends and patterns.

关键词： Translation Accuracy Scalability Instruments Semantics Market research natural language processing Human in the loop

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Research on Maintenance Strategy Generation of Substation Equipment Based on Large Model Retrieval Enhancement Technology

Research on Maintenance Strategy Generation of Substation Eq...

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Smart Grid and Artificial Intelligence (SGAI), International conference on

作者： Xiaodong Wang Linyu Zhang Feng Zhao Jingpo Bai Xiaoguang Huang Xiaohang Zhang Min Jin Dongmei Cui State Grid Information and Communication Industry Group Beijing China Beijing Sgitg Accenture Information Technology Center. Co. Ltd Beijing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the continuous expansion of power grid scale and the increasing complexity of power equipment, substation equipment maintenance decision-making faces challenges such as big data challenges, information complexity, and high decision-making barriers. Traditional maintenance strategy generation methods mainly rely on human expertise and domain knowledge, which are not only inefficient but also easily affected by human factors, leading to inaccurate and delayed decision-making. In recent years, with the rapid development of natural language processing (NLP) technology, especially the emergence of large language models (LLMs), a innovative methodology for the automatic generation of substation equipment maintenance strategies has been provided. This study presents a novel methodology for generating substation maintenance strategies through retrieval-augmented large language model technology By constructing a retrieval-augmented knowledge repository containing a vast amount of historical maintenance cases and professional knowledge, and utilizing the generation capability of large language models, the method achieves rapid and accurate diagnosis of substation equipment faults and automatic generation of maintenance strategies. empirical evaluations demonstrate that this method can significantly improve the efficiency and accuracy of maintenance strategy generation, providing strong technical support for the stable operation of the power system.

关键词： Substations Accuracy Large language models Knowledge based systems Decision making natural language processing Maintenance Complexity theory Tuning Videos

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Leveraging AI to Ensure Authenticity in Student Assignments: A Knowledge-Based Validation and Evaluation Framework 5

Leveraging AI to Ensure Authenticity in Student Assignments:...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Hondarangala, Chinthaka Wickramaarachchi, Dilani University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798331530983

This research explores the development of an AI-driven assessment system designed to evaluate student knowledge while minimizing cheating in educational settings. The system operates in two distinct phases. In Phase 1, AI-generated, contextually relevant questions based on student-submitted assignments assess whether the student has genuinely engaged with the content or used external tools, such as generative AI, without proper understanding. In Phase 2, the system delves deeper into conceptual understanding by aligning submissions with course-specific materials, evaluating relevance, depth, and originality. A fine-tuned large language model (LLM), trained on domain-specific data, enables precise evaluation against academic standards. Results indicate that students demonstrated marked improvements in understanding and performance, validating the effectiveness of the two-phase model. This model encouraged deeper engagement and comprehension, increasing students' knowledge of the material. By addressing the shortcomings of traditional assessment methods, this research introduces a model that reduces the likelihood of students blindly submitting assignments without truly understanding the content. © 2025 IEEE.

关键词： natural language processing systems

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Detecting Bias in Large language Models: Fine-Tuned KcBERT 4th

Detecting Bias in Large Language Models: Fine-Tuned KcBERT

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4th International conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Lee, Jun Koo Chung, Tai-Myoung College of Science Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819787043

The rapid advancement of large language models (LLMs) has brought their natural language processing capabilities to a level comparable to human performance. These models are now extensively used in various societal domains, including education and healthcare. However, despite their versatility, LLMs can produce subjective and normative language, potentially leading to discriminatory outcomes among social groups, particularly through the dissemination of offensive language online. In this paper, we define such phenomena as societal bias and assess ethnic, gender, and racial biases in a model fine-tuned with Korean comments using the Bidirectional Encoder Representations from Transformers (KcBERT) and Korean language Open Data (KOLD) through template-based Masked language Modeling (MLM). To quantitatively evaluate these biases, we use the language Pattern Bias Score (LPBS) and Contextual Bias Score (CBS) metrics. Our results show that, compared to KcBERT, the fine-tuned model exhibits a reduction in ethnic bias but significant alterations in gender and racial biases. To mitigate these societal biases, we propose two methods: First, a data balancing approach during the pre-training phase that adjusts the uniformity of data by aligning the distribution of specific word occurrences and converting surrounding harmful words into non-harmful alternatives. Second, during the in-training phase, we apply Debiasing Regularization by adjusting dropout and regularization parameters, resulting in decreased training loss. Our work highlights the existence of societal biases in Korean language models and demonstrates the importance of language-dependent characteristics in these models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Modeling languages

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework 3

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vvs, Sriharsha Nair, Pranav B. Akarsh, S.V. Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508456

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications. © 2025 IEEE.

关键词： Java programming language

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natural language processing for Sentiment Analysis in ESL Writing: Understanding Emotional Tone in Learner Essays 5

Natural Language Processing for Sentiment Analysis in ESL Wr...

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5th IEEE International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Kumar, M. Vinoth Ansari, Subuhi Kashif Gupta, Deepak Kavitha, P. Kumar, G.M. Prem Bala, B. Kiran Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Technology Department of English Avadi Chennai India Jazan University College of Engineering & Computer Science Jazan Saudi Arabia Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Department of English Erode India Ddgd Vaishnav College University of Madras Pg Department of English Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

In recent years, the significance of sentiment analysis in educational contexts has gained considerable attention, particularly in understanding the emotional expressions of English as a Second language (ESL) learners. Accurately capturing these sentiments is essential for providing targeted support and feedback to enhance student writing skills. This study presents an advanced natural language processing (NLP) framework for sentiment analysis in ESL writing, specifically focusing on understanding emotional tones in learner essays. Utilizing a dataset sourced from Kaggle, which categorizes tweets based on emoticons, the methodology incorporates rigorous data preprocessing techniques, including text cleaning, tokenization, and error correction to enhance data quality. A comprehensive feature extraction process captures linguistic, syntactic, and sentiment-related features using dependency parsing and word embeddings, such as Word2Vec and BERT. The proposed model, implemented in Python, demonstrates a remarkable accuracy of 98.5%, significantly surpassing existing methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), which achieved accuracies of 95.12% and 96.97%, respectively. This enhanced performance highlights the model's ability to effectively capture nuanced emotional expressions in ESL writing. The validation of the model's predictions against human evaluations ensures the reliability and accuracy of the findings. This research not only contributes to the field of sentiment analysis but also informs educational practices, offering targeted interventions to support ESL learners in improving their emotional expression and writing skills. Ultimately, the proposed NLP framework serves as a robust tool for enhancing understanding and assessment of emotional tone in educational contexts. © 2025 IEEE.

关键词： Emotion Recognition

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Mitigating Hallucination in Visual-language Models via Re-balancing Contrastive Decoding 7th

Mitigating Hallucination in Visual-Language Models via Re-ba...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Liang, Xiaoyu Yu, Jiayuan Mu, Lianrui Zhuang, Jiedong Hu, Jiaqi Yang, Yuchen Ye, Jiangnan Lu, Lu Chen, Jian Hu, Haoji Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Although Visual-language Models (VLMs) have shown impressive capabilities in tasks like visual question answering and image captioning, they still struggle with hallucinations. Analysis of attention distribution in these models shows that VLMs tend to processing textual tokens rather than visual tokens. This imbalance of attention distribution causes VLMs to favor textual knowledge in the case of multimodal knowledge conflicts, resulting in differences from the image information. In this paper, we propose Re-Balancing Contrastive Decoding (RBD) method, which employs textual and visual branches to recalibrate attention distribution in VLMs. Specifically, the textual branch injects image noise to stimulate the model's dependency on text, thereby reducing textual bias. Concurrently, the visual branch focuses on the selection of significant tokens, refining the attention mechanism to highlight the primary subject. This dual-branch strategy enables the RBD method to diminish textual bias while enhancing visual information. Experimental results demonstrate that our method, RBD, outperforms the existing methods by the CHAIR and POPE metrics, mitigate hallucinations without reducing the model's general capabilities.

关键词： Multimodal Contrastive Decoding Hallucination

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