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Using language Models for assessment of users’ satisfaction with their partner in Persian 1

Using Language Models for assessment of users’ satisfaction...

引用

1st Workshop on language Models for Low-Resource languages, LoResLM 2025 - co-located at the 31st international conference on Computational Linguistics, COLING 2025

作者： Habibzadeh, Zahra Asadpour, Masoud School of Electrical Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9798891762152

Sentiment analysis, the process of gauging user attitudes and emotions through their textual data, including social media posts and other forms of communication, is a valuable tool for informed decision-making. In other words, by determining whether a statement conveys positivity, negativity, or neutrality, sentiment analysis offers insights into public sentiment regarding a product, individual, event, or other significant topics. This research focuses on the effectiveness of sentiment analysis techniques, using Machine Learning (ML) and natural language processing (NLP) especially pre-trained language models for Persian, in assessing users’ satisfaction with their partner, using data collected from X (formerly Twitter). Our motivation stems from traditional in-person surveys, which periodically analyze societal challenges in Iran. The limitations of these surveys led us to explore Artificial Intelligence (AI) as an alternative solution for addressing contemporary social issues. We collected Persian tweets and utilized data annotation techniques to label them according to our research question, forming the dataset. Our goal also was to provide a benchmark of Persian tweets on this specific topic. To evaluate our dataset, we employed several classification methods, including classical ML models, Deep Neural Networks, and pre-trained language models for Persian. Following a comprehensive evaluation, our results show that BERTweet-FA (one of the pre-trained language models for Persian) emerged as the best performer among the classifiers for assessing users’ satisfaction. This point indicates the ability of language models to understand conversational Persian text and perform sentiment analysis, even in a low-resource language like Persian. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Leveraging LLMs for Attribute Definition and Value Extraction from User-Generated Texts on a C2C E-Commerce Platform 24

Leveraging LLMs for Attribute Definition and Value Extractio...

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Rusli, Andre Oshima, Yuji Yada, Yuki AI/LLM Mercari Inc. Tokyo Japan Engineering Jizai Inc. Tokyo Japan Work Data Platform AI/ML Mercari Inc. Tokyo Japan

ISBN: (纸本)9798400717383

This study explores the application of large language models (LLMs) for Attribute Definition and Value Extraction in C2C e-commerce. We investigate the use of Llama- and Gemma-based open models, employing fine-tuning and post-training quantization techniques, such as QLoRA, and compare them to an LLM available via commercial APIs. Our approach aims to streamline the process of defining key item attributes and extracting their values from user-generated content. We evaluate model performance using metrics like BLEU and F1 scores, demonstrating how employing fine-tuning and post-quantization techniques on open-sourced models can be beneficial in a specific domain. The research also examines the practical implications of these models in a production environment serving millions of monthly active users in Japan, highlighting the challenges and solutions related to latency and resource consumption. © 2024 Copyright held by the owner/author(s).

关键词： Marketplaces

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Design and Implementation of an Intelligent Document Extraction and Review System Based on Multimodal Large language Models 24

Design and Implementation of an Intelligent Document Extract...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Qiu, Han Sun, Chuanqiang Chen, Hongyun Zou, Baoyu Dong, Zizheng Huazhong University of Science and Technology Hubei Wuhan China Shandong Provincial E-Government Big Data Engineering Technology Research Center Shandong Jinan China Shandong Banner Information Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

This study aims to address the issues of low efficiency and high manual involvement in existing government affairs processes by proposing an automated system based on large model *** system integrates modules such as data collection, natural language processing, business rule engine, process generation, and intelligent *** include using large models to transform unstructured government information into executable rules, automatically generating process models and documents, and implementing intelligent approval and automated verification at key *** results indicate that the system significantly enhances the automation level and execution efficiency of government affairs processes, providing a new solution for the intelligent transformation of government management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Efficiency

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Phishing Email Detection Through Machine Learning and Word Error Correction 17

Phishing Email Detection Through Machine Learning and Word E...

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kulal, Deeksha Shiferaw, Leul Niyaz, Quamar Purdue University School of Engineering NorthwestIN46323 United States

ISBN: (纸本)9798331531195

Phishing is considered as one of the effective fraudulent activities on the Internet. Numerous machine learning (ML) based models have been implemented for detecting phishing emails using publicly available datasets (e.g. Nazario, Millersmile). These datasets exhibit noticeable flaws, such as poor grammar structure and incorrect word usage, which are learned as critical features by ML models. However, the grammatical quality and overall structure of phishing emails have been significantly improved with large language models (LLMs). This shift could reduce the effectiveness of traditional grammatical cues for identifying phishing emails, as the improved grammatical quality makes these emails appear more legitimate. In order to handle this challenge, we pose the following research question: Can an ML-based phishing detection model, enhanced with word correction and splitting techniques, effectively identify phishing emails? To answer this question, we implement a phishing detection system considering misspelled word correction and splitting of combined words during the data processing stage and employing state-of-art natural language processing techniques. To enhance the robustness of models, datasets from diverse sources and timelines are used for model training and deployment. © 2025 IEEE.

关键词： Phishing

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natural language processing-based Translating Style Research of Two Representative Translating Versions from Data-Driven Methodology

Natural Language Processing-based Translating Style Research...

引用

2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Lin, Hao Han, Zhonghua School of Foreign Languages Shenyang Jianzhu University Shenyang110168 China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

As one of Chinese famous classical works, Hongloumeng has been translated by different languages and accepted by most scholars in the world. At present, there are two famous translating versions which are broadly accepted since their translations are loyal and closely related to source text, and translators are married couple of Yang Hsien-yi, Gladys and British translator Hawks. In order to comprehensively study characteristics of these two translating versions, this paper applies corpus search software Antconc 3.2.1w to study translating characteristics of vocabulary, syntax and context so as to make comparative analysis and deeply understand their translating characteristics. Based on above analysis, this paper discovers that Yang Hsien-yi and Gladys prefer literal language and their translation is more loyal to source text but it is difficult for western readers to deeply understand the content. However, in terms of Hawks' translation, it is widely accepted by western readers because of its colloquial language but the content is less loyal to source text than Yang Hsien-yi and Gladys' translation. © 2025 SPIE.

关键词： Data assimilation

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The Role of Large language Models Within Construction Facilities Lifecycle Data Management 2nd

The Role of Large Language Models Within Construction Facili...

引用

2nd international Scientific conference on Recent Advances in Architecture and Construction, ICRAAC 2024

作者： Chaplygin, Eugeny Losev, Konstantin Moscow129337 Russia

ISBN: (纸本)9783031829376

Introduction: The modern construction industry faces the challenge of managing vast volumes of fragmented data, which complicates its integration and analysis across all stages of the construction lifecycle. The use of artificial intelligence, particularly large language models (LLM), offers a promising approach to address these issues. The relevance of the study lies in the need to optimize data processing workflows using LLM to improve the efficiency of construction project management. Materials and Methods: The study examines three methods for applying LLM: Retrieval-Augmented Generation (RAG) for unstructured data, Text-to-SQL for structured data in relational databases, and knowledge Map for processing sensitive unstructured data. Results: Testing demonstrated that RAG effectively retrieves information from unstructured sources, Text-to-SQL ensures precise access to data in relational databases, and knowledge Map minimizes errors when handling hierarchical data structures. These methods complement each other, enabling LLM to work with a wide range of data types and improving the accuracy of the responses. Conclusion: The application of LLM in construction data management opens new opportunities for automation and improving the precision of data processing. Further integration of LLM with advanced technologies, such as digital twins and the Internet of Things, has the potential to create more efficient systems for managing construction projects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Relational database systems

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LLM-In-The-Loop: A Framework for Enhancing Drilling Data Analytics

LLM-In-The-Loop: A Framework for Enhancing Drilling Data Ana...

引用

2025 SPE/IADC international Drilling conference and Exhibition, DC 2025

作者： Al Amin, S.T. Wu, K. Mathur, A. Koritala, C. Nabors Corporate Services HoustonTX United States

ISBN: (纸本)9781959025641

Analyzing and visualizing drilling data is critical for optimizing operations and informed decision-making. As the drilling data are mostly structured data coming from downhole sensors and drilling equipments, traditional methods to analyze these data often require programming skills, causing delays and repetitive tasks for experts. On the other hand, advancement in large language models (LLMs) has enabled to chat with our internal data. Human-like or natural language can be used to extract meaningful and important information from large amount of data. With these motivations in mind, we propose a novel approach leveraging large language models (LLMs) to enhance drilling data analytics. By enabling natural language queries for complex data analysis, broader oil and gas users without traditional data analysis expertise can take advantage of it. Stakeholders and relevant personnels can easily perform data analysis and visualization without the need for specialized programming knowledge. We introduce a framework to analyze drilling data with the help of LLM models. Our method retrieves relevant information and similarity pairs which are integrated into the prompt along with the user query. The enhanced prompt is subsequently fed to an LLM, generating source code which is executed by an agent through plans, actions and feedback. The processed answer is returned to the user. Our approach enables real-time, efficient, and accurate analysis of drilling data, making it accessible to all users. Experimental results indicate that LLM models exhibit superior accuracy when prompts are enriched with similarity pairs and contextual information, compared to processing raw user queries directly. We show that our framework can handle a variety of query types, including basic statistics, summarization, aggregation, comparison, filtering, correlation, and time-series analysis. We demonstrate high accuracy of our framework in these tasks using both zero-shot and few-shot learning appro

关键词： Structured Query language

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Text Categorization Can Enhance Domain-Agnostic Stopword Extraction 3rd

Text Categorization Can Enhance Domain-Agnostic Stopword Ext...

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3rd international conference on language processing and knowledge Management, LPKM 2024

作者： Turki, Houcemeddine Etori, Naome A. Hadj Taieb, Mohamed Ali Omotayo, Abdul-Hakeem Emezue, Chris Chinenye Ben Aouicha, Mohamed Awokoya, Ayodele Lawan, Falalu Ibrahim Nixdorf, Doreen Data Engineering and Semantics Research Unit University of Sfax Sfax Tunisia University of Minnesota MinneapolisMN United States University of California DavisCA United States Technical University of Munich Munich Germany McPherson University Lagos Nigeria Kaduna State University Kaduna Nigeria Masakhane Research Community Johannesburg South Africa

ISBN: (纸本)9783031850660

This paper investigates the role of text categorization in streamlining stopword extraction in natural language processing (NLP), specifically focusing on nine African languages alongside French. By leveraging the MasakhaNEWS, African Stopwords Project, and MasakhaPOS datasets, our findings emphasize that text categorization effectively filters out domain-specific keywords for most examined languages, allowing a simpler identification of stopwords. Nevertheless, linguistic variances result in lower detection rates for specific languages. Interestingly, we find that while over 40% of stopwords are common across news categories, less than 15% are unique to a single category. Uncommon stopwords add depth to the text, but their classification as stopwords depends on context. Therefore, combining statistical and linguistic approaches creates comprehensive stopword lists, highlighting the value of our hybrid method. This research enhances NLP for African languages and underscores the importance of text categorization in stopword extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： African languages Domain-agnostic Stopwords Stopword Extraction Text Categoization

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Reasoning with Trees: Faithful Question Answering over knowledge Graph 31

Reasoning with Trees: Faithful Question Answering over Knowl...

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31st international conference on Computational Linguistics, COLING 2025

作者： Shen, Tiesunlong Wang, Jin Zhang, Xuejie Cambria, Erik School of Information Science and Engineering Yunnan University Kunming China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761964

Recent advancements in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks. This study introduces Reasoning with Trees (RwT), a novel framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability. RwT reformulates knowledge graph question answering (KGQA) as a discrete decision-making problem, leveraging Monte Carlo Tree Search (MCTS) to iteratively refine reasoning paths. This approach mirrors human-like reasoning by dynamically integrating the LLM's internal knowledge with external KG information. We propose a real-data guided iteration technique to train an evaluation model that assesses action values, improving the efficiency of MCTS. Experimental results on two benchmark KGQA datasets demonstrate that RwT significantly outperforms existing state-of-the-art methods, with an average performance improvement of 9.81%. Notably, RwT achieves these improvements without requiring complete LLM retraining, offering a more efficient and adaptable approach to enhancing LLM reasoning capabilities. © 2025 Association for Computational Linguistics.

关键词： knowledge graph

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Enhancing Medical Diagnosis with Fine-Tuned Large language Models: Addressing Cardiogenic Pulmonary Edema (CPE) 24

Enhancing Medical Diagnosis with Fine-Tuned Large Language M...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Chiu, Yen-Jung Chuang, Chao-Chun Hwa, Kuo-Yuan Department of Biomedical Engineering Ming Chuan University Taipei Taiwan National Center For High-Performance Computing Hsinchu Taiwan Department of Molecular Science and Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798400718274

Large language Models (LLMs) have revolutionized natural language processing (NLP) with significant advancements in text generation. LLMs often struggle with complex domain-specific tasks, such as medical report analysis, despite their capabilities. This study focuses on enhancing LLM performance for medical applications, particularly in diagnosing and managing cardiogenic pulmonary edema (CPE). This research explores fine-tuning LLMs to develop a real-time CPE chatbot for Intensive Care Units (ICUs). The chatbot aims to provide diagnostic suggestions and explanations based on patient data. In the results, the LLaMa3-8B model performed better in predicting patients' CPE stage and keyword extraction. The accuracies achieved 72% and 86%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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