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MMTP: Meta-learning-based Multi-Textual Prompt Tuning for Visual-language Models

MMTP: Meta-learning-based Multi-Textual Prompt Tuning for Vi...

引用

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

作者： Sun, Fangtong Zhu, Junjie Fan, Zunlin Li, Yiying Wang, Zhiyuan Yang, Ke Beijing100091 China

ISBN: (纸本)9798350368741

Pre-trained Visual-language Models (VLMs) have demonstrated powerful performance on various downstream tasks. Recently, many prompt tuning methods represented by Context Optimization (CoOp) have effectively adapted VLMs to few-shot tasks. However, the CoOp-based methods suffer from overfitting to base classes, which impairs the model's generalization to new classes. Considering that meta-learning excels at generalizing to new classes, we combine meta-learning with CoOp-like vision-language model fine-tuning methods to improve performance on few-shot generation tasks. In this paper, we present a novel Meta-learning-based Multi-Textual Prompt tuning (MMTP) method, which learns multiple textual prompts leveraging meta-learning to enhance the visual-language model's representation and generalization capabilities. Specifically, we introduce multi-textual prompts to enhance the representation of the model for improving the recognition of base classes. Simultaneously, we employ meta-learning to optimize prompt training, bolstering the model's generalization to new classes. Extensive experiments demonstrate the superiority of our method under base-to-new generalization and cross-domain generalization settings. Furthermore, we also conduct ablation studies to validate the effectiveness of each component. © 2025 IEEE.

关键词： few-shot tasks meta-learning prompt tuning visual-language models

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LabelIT: A Multi-cloud Resource Label Unification Tool 30th

LabelIT: A Multi-cloud Resource Label Unification Tool

引用

30th International conference on Cooperative Information Systems, CoopIS 2024

作者： Mechouche, Jeremy Mokni, Marwa Ramusat, Yann Devoteam Research 1 Rue Galvani Massy91300 France

ISBN: (纸本)9783031813740

In cloud environments, labels are often defined by cloud architects to categorise and describe their resources, such as virtual machines, storage and network components. These labels play a crucial role in organising, managing and tracking resources, making it easier to identify and analyse them. However, these labels are often inconsistent, abbreviated, or misspelled, leading to potential mismanagement of cloud resources. In this demonstration, we present LabelIT Kubernetes-based tool designed to standardize resource labels for improved cloud resource management. The label unification tool system is built using NLP (natural language processing) based analysis methods that combine syntactic and semantic similarity processing for accurate unification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud computing Cloud resource management Labels Multi-cloud natural language processing

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Bridging Domain Knowledge and Process Discovery Using Large language Models 22nd

Bridging Domain Knowledge and Process Discovery Using Large ...

引用

22nd International conference on Business Process Management (BPM)

作者： Norouzifar, Ali Kourani, Humam Dees, Marcus van der Aalst, Wil M. P. RWTH Univ Aachen Germany Fraunhofer FIT St Augustin Germany UWV Employee Insurance Agcy Amsterdam Netherlands

ISBN: (纸本)9783031786655;9783031786662

Discovering good process models is essential for different process analysis tasks such as conformance checking and process improvements. Automated process discovery methods often overlook valuable domain knowledge. This knowledge, including insights from domain experts and detailed process documentation, remains largely untapped during process discovery. This paper leverages Large language Models (LLMs) to integrate such knowledge directly into process discovery. We use rules derived from LLMs to guide model construction, ensuring alignment with both domain knowledge and actual process executions. By integrating LLMs, we create a bridge between process knowledge expressed in natural language and the discovery of robust process models, advancing process discovery methodologies significantly. To showcase the usability of our framework, we conducted a case study with the UWV employee insurance agency, demonstrating its practical benefits and effectiveness.

关键词： Process Mining Process Discovery Process Knowledge Large language Models

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Can Large language model analyze financial statements well? 9

Can Large language model analyze financial statements well?

引用

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, Xinlin Brorsson, Mats Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg 29 Av. John F. Kennedy KirchbergL-1855 Luxembourg

ISBN: (纸本)9798891762091

Since GPT-3.5’s release, large language models (LLMs) have made significant advancements, including in financial analysis. However, their effectiveness in financial calculations and predictions is still uncertain. This study examines LLMs’ ability to analyze financial reports, focusing on three questions: their accuracy in calculating financial ratios, the use of these metrics in DuPont analysis and the Z-score model for bankruptcy prediction, and their effectiveness in predicting financial indicators with limited knowledge. We used various methods, including zero-shot and few-shot learning, retrieval-augmented generation (RAG), and fine-tuning, in three advanced LLMs and compared their outputs to ground truth and expert predictions to assess their calculation and predictive abilities. The results highlight both the potential and limitations of LLMs in processing numerical data and performing complex financial analyses. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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In-Context Multitask Learning for Few-shot Fine-tuning of Large language Models in Traditional Chinese Medicine Tongue Diagnosis

In-Context Multitask Learning for Few-shot Fine-tuning of La...

引用

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

作者： Fu, Changzeng Fu, Zelin Yan, Shaojun Lyv, Xiaoyong Zhao, Yuliang Northeastern University China Osaka University Japan Hebei Key Laboratory of Marine Perception Network and Data Processing @ NEUQ China

ISBN: (纸本)9798350368741

Tongue diagnosis is integral to Traditional Chinese Medicine (TCM) for evaluating a patient's body constitution. Yet, this field faces challenges such as indirect constitution diagnosis, a dearth of labeled datasets, and the complexities of few-shot learning. Existing studies focus mainly on analyzing tongue color and coating, rather than directly analyzing the body constitution from the patient's tongue. Moreover, the lack of publicly available datasets with constitution labels impedes model development for constitution diagnosis. The resource-intensive process of creating large-scale labeled datasets calls for efficient training methods on small datasets. Addressing these issues, this paper presents an In-Context Multitask Learning approach to improve few-shot fine-tuning accuracy for Large language Models (LLMs) in constitution diagnosis. We created a dataset for color-coating analysis and a few-shot dataset with constitution labels and a structured prompt-label framework, allowing LLMs to learn from varied datasets. Our method shows improved performance over traditional methods in constitution diagnosis and significantly boosts LLMs' generalization and robustness in TCM's complex diagnostic tasks, providing a viable path for automating tongue diagnosis. © 2025 IEEE.

关键词： Few-shot Fine-Tuning Large language Models Multitask Learning Tongue Diagnosis

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Semantic Knowledge Enhanced and Global Pointer Optimized Method for Medical Nested Entity Recognition 13th

Semantic Knowledge Enhanced and Global Pointer Optimized Met...

引用

13th International conference on natural language processing and Chinese Computing

作者： Song, Yilin Kong, Fang Soochow Univ Lab Nat Language Proc Suzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

In the medical field, unstructured medical text holds rich medical knowledge. Identifying medical entities in this text accurately is crucial for structured medical databases, knowledge graphs, and intelligent diagnostic systems. Medical text has unique features, making it hard for traditional NER methods to identify complex medical entities. In particular, the recognition of nested entities within medical text poses a significant challenge, as it requires systems to recognize and understand the complex hierarchical relationships between entities, placing higher demands on traditional entity recognition systems. To overcome the challenges of nested entity recognition in medical text, we propose a method that combines semantic knowledge enhancement and global pointer optimization. Initially, we incorporate semantic prior knowledge of entity categories, capturing the interplay between labels and text by integrating label relationships. This allows us to obtain candidate entity information enriched with integrated label details. Following this, we establish a classification module to evaluate and score these candidate entities along with their labels, enabling entity prediction. To address nested entities, we introduce a Efficient GlobalPointer module that computes the likelihood of each text span being a specific entity type, thus bolstering nested entity recognition. By merging the outputs from both modules, we arrive at the final predicted entities. Experimental results indicate that our method excels on two flat entity datasets, CMedQANER and CCKS2017, as well as on the nested entity dataset CMeEE. Compared to baseline models, our approach demonstrates notable performance enhancements.

关键词： Named Entity Recognition Nested Entity Recognition Medical Named Entity Recognition

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An Automated Grading System for Competition Theses Assisted by NLP Models 24

An Automated Grading System for Competition Theses Assisted ...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xue, Sijuan Duan, Xu Yang, Manqi College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

As the digital transformation of education continues to advance, the inefficiency and subjectivity of traditional manual scoring methods have become increasingly prominent. To address this issue, this study developed an automated scoring process. The process includes several key steps: reading text files, training latent Dirichlet allocation (LDA) topic models, matching texts with conditional sentence libraries, and performing natural language processing (NLP) analysis. The scoring method used in this study follows the standards proposed by advocates of text scoring conditions to comprehensively evaluate the completeness and accuracy of the text. The entire paper is comprehensively evaluated from four dimensions: completeness, content, summary quality, and writing ability. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases 12th

Key Algorithms for Keyphrase Generation: Instruction-Based ...

引用

12th International conference on Analysis of Images, Social Networks and Texts, AIST 2024

作者： Glazkova, Anna Morozov, Dmitry Garipov, Timur University of Tyumen Tyumen Russia Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9783031880353

Keyphrase selection is a challenging task in natural language processing that has a wide range of applications. Adapting existing supervised and unsupervised solutions for the Russian language faces several limitations due to the rich morphology of Russian and the limited number of training datasets available. Recent studies conducted on English texts show that large language models (LLMs) successfully address the task of generating keyphrases. LLMs allow achieving impressive results without task-specific fine-tuning, using text prompts instead. In this work, we access the performance of prompt-based methods for generating keyphrases for Russian scientific abstracts. First, we compare the performance of zero-shot and few-shot prompt-based methods, fine-tuned models, and unsupervised methods. Then we assess strategies for selecting keyphrase examples in a few-shot setting. We present the outcomes of human evaluation of the generated keyphrases and analyze the strengths and weaknesses of the models through expert assessment. Our results suggest that prompt-based methods can outperform common baselines even using simple text prompts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： natural language processing systems

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Diagram Formalization Enhanced Multi-Modal Geometry Problem Solver

Diagram Formalization Enhanced Multi-Modal Geometry Problem ...

引用

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

作者： Zhang, Zeren Cheng, Jo-Ku Deng, Jingyang Tian, Lu Ma, Jinwen Qin, Ziran Zhang, Xiaokai Zhu, Na Leng, Tuo School of Mathematical Sciences Peking University Beijing100871 China 01.AI Beijing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368741

Mathematical reasoning remains an ongoing challenge for AI models, especially for geometry problems, which require both linguistic and visual signals. As the vision encoders of most MLLMs are trained on natural scenes, they often struggle to understand geometric diagrams, performing no better in geometry problem-solving than LLMs that only process text. This limitation is further amplified by the lack of effective methods for representing geometric relationships. To address these issues, we introduce the Diagram Formalization Enhanced Geometry Problem Solver (DFE-GPS), a new framework that integrates visual features, geometric formal language, and natural language representations. Specifically, we propose a novel synthetic data approach and construct a large-scale geometric dataset, SynthGeo228K, annotated with formal and natural language captions, designed to enhance the vision encoder to understand geometric structures better. Our framework improves MLLMs' ability to process geometric diagrams and extends their application to open-ended tasks on the formalgeo7k dataset. © 2025 IEEE.

关键词： geometric diagram formalization geometry problem solver mathematical reasoning multi-modal large language model

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Automatic Text Simplification for the Legal Domain in Brazilian Portuguese 34th

Automatic Text Simplification for the Legal Domain in Brazil...

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34th Brazilian conference on Intelligent Systems

作者： Pereira, Francielle Vasconcellos Frazao, Ana Moreira, Viviane P. UFRGS Porto Alegre Inst Informat Porto Alegre RS Brazil USP Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783031790379;9783031790386

Legal and juridical documents such as rulings, laws, agreements, and contracts contain domain-specific terms and jargon, long and complex sentences that may be difficult to understand for laypeople without domain expertise, reading issues, or with a low education level. The simplification of these documents has been a concern for several years, aiming to democratize access to justice. Courts are already adopting simpler language, especially in documents aimed at laypeople, such as warrants and notifications, to enhance inclusion and clarity. Automatic textual simplification, a subfield of natural language processing, seeks to make complex texts more accessible. This paper explores the task of automatic text simplification in Portuguese for the legal domain. The main challenge here is the lack of datasets containing complex sentences and their simplified versions. This work investigates how existing datasets, methods, and metrics used for text simplification perform applied to legal texts in Portuguese. We present qualitative and quantitative analyses using five models. The results show that GPT-based models have the best results, but fine-tuning with domain data is a viable open-source alternative.

关键词： Automatic Text Simplification Legal Texts natural language processing Plain language

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