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Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers 9

Proxy Tuning for Financial Sentiment Analysis: Overcoming Da...

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Joint Workshop of the 9th Financial technology and Natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Yuxiang Wang, Yuchi Liu, Yi Bao, Ruihan Harimoto, Keiko Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Mizuho Securities Co. Ltd. Japan

ISBN: (纸本)9798891762091

Financial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity of high-quality datasets. To address these issues, we leverage recent advancements in large language models (LLMs) and propose to adapt proxy tuning for financial sentiment analysis. Proxy tuning efficiently transfers knowledge from a pre-trained expert model to a controllable base model by incorporating logit differences, steering the base model toward the desired sentiment representation. Our method offers significant advantages: (1) it is training-free, reducing computational demands and data dependency;(2) it achieves promising performance, with a 36.67% improvement over the base model and over 90% of the tuned model’s performance;and (3) it is highly adaptable, functioning in a plug-and-play manner without requiring access to model architectures or weights. these results demonstrate the potential of proxy tuning as an efficient and practical solution for financial sentiment analysis in data-scarce scenarios. © 2025 Association for Computational Linguistics.

关键词： Finance

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web Development and Evaluation of Instructional Objectives’ Quality Using Natural language processing 6th

Web Development and Evaluation of Instructional Objectives’...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mohanta, Jyotiprava Jain, Mahim Jain, Mahim M. Mandal, Shyamal Kumar Das Advance Technology Development Centre Indian Institute of Technology Kharagpur India Aerospace Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819780891

Outcome-based education is currently recommended in every field of education by the accreditation bodies. Outcome-based education is the process of developing the curriculum in a well-defined framework. the stated outcome or the instructional objective should be properly defined for successful implementation. Here, a methodology is proposed where the curriculum designer can check whether the instructional objective is properly designed. the methodology consists of website development and a machine learning model (ML) using natural language processing. the input to the model is an instructional objective statement, and the result will be in three categories: poor, good, and perfect. the website consists of a Flask-written backend and a Next. js-developed frontend. the backend was integrated with the ML model. For the model, the Bidirectional Encoder Representations from Transformers model is used. the designed model was applied to 50 instructional objective datasets for training the model and 40 datasets for testing the prediction. the BERT model was found to be effective. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Enriching RDF Data with LLM Based Named Entity Recognition and Linking on Embedded Natural language Annotations 6th

Enriching RDF Data with LLM Based Named Entity Recognition ...

引用

6th international conference on Knowledge Graphs and Semantic web, KGSWC 2024

作者： Freund, Michael Dorsch, Rene Schmid, Sebastian Wehr, thomas Harth, Andreas Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Nürnberg Germany

ISBN: (纸本)9783031812200

In this paper, we present a processing pipeline for transforming natural language annotations in RDF graphs into machine-readable and interoperable semantic annotations. the pipeline uses Named Entity Recognition (NER) and Entity Linking (EL) techniques based on a foundational Large language Model (LLM), combined with a Knowledge Graph (KG) based knowledge injection approach for entity disambiguation and self-verification. through a running example in the paper, we demonstrate that the pipeline can increase the number of semantic annotations in an RDF graph derived from information contained in natural language annotations. the evaluation of the proposed pipeline shows that the LLM-based NER approach produces results comparable to those of fine-tuned NER models. Furthermore, we show that the pipeline using a chain-of-thought prompting style with factual information retrieved via link traversal from an external KG achieves better entity disambiguation and linking than both a pipeline without chain-of-thought prompting and an approach relying only on information within the LLM. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Application of Computer information technology in Intelligent Analysis and Decision-Making Support of Diagnosis and Treatment Data 6th

Application of Computer Information Technology in Intelligen...

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6th international conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Gao, Yan Zhang, Yinsong Yunnan Agricultural University Yunnan China

ISBN: (纸本)9783031882869

In view of the problem that TCM diagnosis and treatment data are complex, diverse and lack unified standards, this paper introduced computer information technology, combined with NLP and LSTM models, to improve the intelligent analysis ability of TCM diagnosis and treatment data, and provide scientific support for clinicians through an auxiliary decision-making system, thereby improving the efficiency and accuracy of diagnosis and treatment. First, this paper collected and integrated a large amount of medical records, treatment records, prescription information and other data from different TCM diagnosis and treatment platforms and medical institutions, standardizes data in different formats, and used natural language processing (NLP) technology for semantic analysis and data cleaning. then, it built a classification and prediction model based on LSTM (Long Short Time Memory) to realize intelligent diagnosis and treatment recommendation generation for common diseases in view of the complex symptoms and individual differences unique to TCM diagnosis and treatment. Finally, based on the previous analysis and model results, an auxiliary decision system was developed to provide doctors with auxiliary decision suggestions in the treatment of complex symptoms and medication regimen recommendations during the diagnosis and treatment process. the results showed that after the use of the intelligent auxiliary decision system, the diagnosis and treatment time reduction rate was between 21.8% and 23.0%, and the accuracy rate reached 88.0%. Compared with traditional manual analysis methods, the efficiency of TCM diagnosis and treatment data processing has been significantly improved after the application of computer information technology. the introduction of computer information technology provides a new solution for the intelligent analysis and decision-making support of TCM diagnosis and treatment data, effectively solving the problems of complex, heterogeneous and insuffici

关键词： Data reduction

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Insights on Improving Reasoning and Increasing Capabilities in Large language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. these models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. these models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Enhancing Radiology Report Interpretation with Large-Scale language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

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27th international conference on advanced Communications technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. this study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. the resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. these findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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Process Modeler vs. Chatbot: Is Generative AI Taking over Process Modeling?

Process Modeler vs. Chatbot: Is Generative AI Taking over Pr...

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international Workshops which were held in conjunction with the 6th international conference on Process Mining, ICPM 2024

作者： Klievtsova, Nataliia Benzin, Janik-Vasily Mangler, Juergen Kampik, Timotheus Rinderle-Ma, Stefanie TUM School of Computation Information and Technology Technical University of Munich Garching Germany SAP Signavio Berlin Germany

ISBN: (纸本)9783031822247

Large language models (LLMs) have become a promising tool for automating complex tasks such as process model generation from text. In order to evaluate the capabilities of LLMs in generating process models, it is crucial to provide means to assess the output quality. A few studies have already provided key performance indicators for assessing aspects such as completeness of the models in a quantitative way. In this paper, we focus on the qualitative assessment of generated process models generated by LLMs based on a user survey. By analyzing user preferences, we aim to determine whether LLM-generated process models meet the needs and expectations of experts. Our analysis reveals that 60% of users, regardless of their modeling experience, prefer LLM-generated models over human-created ground truth models. © the Author(s) 2025.

关键词： Modeling languages

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Comparative Analysis of Different Models for Sarcastic and Non-sarcastic Text Detection Using NLP 6th

Comparative Analysis of Different Models for Sarcastic and N...

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6th international conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Verma, Deval Kumar, Pranay Agarwal, Manish Verma, Gaurav Sombatteera, Sujanya Butdisuwan, Sujin Bennett University Greater Noida India Jaypee Institute of Information Technology Noida India Shinawatra University Samkhok Thailand

ISBN: (纸本)9789819783281

Sarcasm recognition is another aspect of natural language processing (NLP) used for sentiment analysis. It uses mathematical inference to show and categorize a word or phrase’s polarity for sardonic. In literature, sarcasm detection has been applied to various datasets such as the Reddit corpus and Twitter datasets, which mostly contain noisy language and labels. this work aims to identify sarcasm in the following datasets: news headlines, generic, rhetorical questions, hyperbole, English and Arabic text to overcome these issues. this study compares various models intended to distinguish between sardonic and non-sardonic information to understand this intricate problem efficiently. Moreover, standard datasets are trained using deep learning techniques, including transformers like GPT and BERT. the transformer-based models (GPT and BERT) outperform other models, with F1-scores of 87% and 85%, respectively. the model with the lowest performance is the logistic regression model, having a F1-score of 73%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Scalable Summarization of Long-Form Video Transcripts using NLP 6

Scalable Summarization of Long-Form Video Transcripts using ...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Minu, M.S. Mirajkar, Feroza D. Mohanaprakash, T.A. Kaviya, D. Jasmine Fathima, K. Maha, S. Department of CSE SRM Institute of Science and Technology Ramapuram Chennai India Department of Electronics and Communication Engineering FoET Khaja Bandanawaz University Kalaburagi India Department of CSE SOET CMR University Bengaluru India SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331522667

this research designs a system that, with the help of NLP, would sum up the YouTube video transcripts properly without losing any important information. Day in day out, time keeps on recording increasing numbers of videos that are housed in internet-based platforms. Educating is the most prominent explanation for all the global content being available and accessible. One might also train on the different networks such as Google, Facebook, Instagram, YouTube, and once again Google. the underlying truth that the viewer has to watch the entire film to really understand and appreciate the broader perspective shows one key element—is comparable with images where so much information and data can be extracted and understood from just one frame;it indicates that data extraction from videos is a both an important and challenging question. thus, the aim of this research project is to comfortably shorten and summarize the length of the video transcript so that it can easily be understood. the proposed approach is to get transcripts by using the link given from the video provided by the user and then use TD-IDF combined with the BART transformer in order to text summarize. the model developed here successfully satisfies the consideration of video URLs shared by the user and the exact summary duration needed. At the end, this results in the detailed summary of the transcript for the videos. Comparing with the other proposed methods put forward, the final text translation has been found to be present much earlier than what the results initially suggested. Moreover, a significant point has to be added that the final text absolutely depicts the theme of the movie without any kind of change or alteration. © 2025 IEEE.

关键词： Inverse Document Frequency User-provided video link Natural language processing Term Frequency Video summarization

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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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