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DAP: DOMAIN-AWARE PROMPT LEARNING FOR VISION-AND-language NAVIGATION 49

DAP: DOMAIN-AWARE PROMPT LEARNING FOR VISION-AND-LANGUAGE NA...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Liu, Ting Hu, Yue Wu, Wansen Wang, Youkai Xu, Kai Yin, Quanjun Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Following language instructions to navigate in unseen environments is a challenging task for autonomous embodied agents. With strong representation capabilities, pretrained vision-and-language models are widely used in VLN. However, most of them are trained on web-crawled general-purpose datasets, which incurs a considerable domain gap when used for VLN tasks. To address the problem, we propose a novel and model-agnostic Domain-Aware Prompt learning (DAP) framework. For equipping the pretrained models with specific object-level and scene-level cross-modal alignment in VLN tasks, DAP applies a low-cost prompt tuning paradigm to learn soft visual prompts for extracting in-domain image semantics. Specifically, we first generate a set of in-domain image-text pairs with the help of the CLIP model. Then we introduce soft visual prompts in the input space of the visual encoder in a pretrained model. DAP injects in-domain visual knowledge into the visual encoder of the pretrained model in an efficient way. Experimental results on both R2R and REVERIE show the superiority of DAP compared to existing state-of-the-art methods.

关键词： vision-and-language multimodal representation

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Intelligent Content Summarization System Using natural language processing 8

Intelligent Content Summarization System Using Natural Langu...

引用

8th ieee international conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Tamboli, Amaan Kadam, Archana Tekam, Abhinav Talegaonkar, Ayush Wakchaure, Bhushan Pimpri Chinchwad College of Engineering Pune Computer Engineering Pune India

ISBN: (纸本)9798350391770

This project proposes an NLP-based Intelligent information Summarization System that reduces the effort required for comprehending and summarizing instructional material, maximizing engineering students' study time. By using state-of-the-art natural language processing (NLP) techniques, the system allows students to efficiently extract relevant information from online videos, thereby transforming the learning experience. By automating the material summary, it both demonstrates the revolutionary potential of natural language processing (NLP) in education and helps students feel less stressed. This project fosters academic success by allowing students to engage more fully with the course materials. It also symbolizes a paradigm shift toward more efficient and accessible education. The approach may promote academic success and advancement for students. © 2024 ieee.

关键词： Students

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Evaluating Class Membership Relations in knowledge Graphs Using Large language Models

Evaluating Class Membership Relations in Knowledge Graphs Us...

引用

21st international conference on The Semantic Web (ESWC)

作者： Allen, Bradley P. Groth, Paul T. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031789519;9783031789526

A backbone of knowledge graphs are their class membership relations, which assign entities to a given class. As part of the knowledge engineering process, we propose a new method for evaluating the quality of these relations by processing descriptions of a given entity and class using a zero-shot chain-of-thought classifier that uses a natural language intensional definition of a class. We evaluate the method using two publicly available knowledge graphs, Wikidata and CaLiGraph, and 7 large language models. Using the gpt-4-0125-preview large language model, the method's classification performance achieves a macro-averaged F1-score of 0.830 on data from Wikidata and 0.893 on data from CaLiGraph. Moreover, a manual analysis of the classification errors shows that 40.9% of errors were due to the knowledge graphs, with 16.0% due to missing relations and 24.9% due to incorrectly asserted relations. These results show how large language models can assist knowledge engineers in the process of knowledge graph refinement. The code and data are available on Github (https://***/bradleypallen/evaluating-kg-class-memberships-using-llms).

关键词： knowledge engineering large language models knowledge graph refinement natural language generation

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ProDepDet: Out-of-domain knowledge Transfer of Pre-trained Large language Models for Depression Detection in Text-Based Multi-Party Conversations

ProDepDet: Out-of-domain Knowledge Transfer of Pre-trained L...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Priyadarshana, Y. H. P. P. Liang, Zilu Piumarta, Ian Kyoto Univ Adv Sci Kyoto Japan

ISBN: (纸本)9798350359329;9798350359312

Parameter-efficient, soft, and prompt- based tuning methods have received increasing attention in various downstream tasks due to the high cost of traditional fine-tuning methods in pre-trained language models (PLM). Prompt tuning (PT) is one such effective mechanism which has achieved remarkable performance in transferring the acquired knowledge of a PLM to perform an unseen task within the same domain using task-specific prompts and informative instructions. Even though most prior work considers in-domain knowledge transfer using PT, much work remains to be done for PT-based out-of-domain knowledge transfer. In this study we propose ProDepDet, a novel framework specifically designed to use a PLM's knowledge about structure and semantic modelling in multi-party conversations to perform the unseen, out-ofdomain task of depression detection. To our knowledge, this study is the first attempt to adapt the acquired knowledge of a PLM for out-of-domain task modelling using PT-based crosstask transferability. Experiments on few-shot and full data settings across multiple benchmark datasets demonstrate the superiority of our PT framework in two downstream tasks including depressed utterance classification and depressed speaker identification.

关键词： prompt task transferability natural language processing deep learning discourse analysis mental health

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Towards Safe, Secure, and Usable LLMs4Code 24

Towards Safe, Secure, and Usable LLMs4Code

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46th international conference on Software engineering: Companion, ICSE-Companion 2024

作者： Al-Kaswan, Ali Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400705021

Large language Models (LLMs) are gaining popularity in the field of natural language processing (NLP) due to their remarkable accuracy in various NLP tasks. LLMs designed for coding are trained on massive datasets, which enables them to learn the structure and syntax of programming languages. These datasets are scraped from the web and LLMs memorise information in these datasets. LLMs for code are also growing, making them more challenging to execute and making users increasingly reliant on external *** aim to explore the challenges faced by LLMs for code and propose techniques to measure and prevent memorisation. Additionally, we suggest methods to compress models and run them locally on consumer hardware. © 2024 ieee Computer Society. All rights reserved.

关键词： natural language processing systems

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Toxicity Detection Using State of the Art natural language Methodologies 8

Toxicity Detection Using State of the Art Natural Language M...

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8th international conference on Control and Robotics engineering (ICCRE)

作者： Keskin, Enes Faruk Acikgoz, Erkut Dogan, Gulustan Turkish Aeronaut Assoc Univ Dept Comp Engn Ankara Turkiye Atilim Univ Dept Ind Engn Ankara Turkiye Univ North Carolina Wilmington Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9798350345650

In this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.

关键词： language models bert transformers natural language processing supervised machine learning algorithms deep learning

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Identify the Current Demanded knowledge, Skills, and Abilities (KSA) in IT through Analysis of Job Advertisements in Sri Lanka: A Framework Using natural language processing Techniques 4

Identify the Current Demanded Knowledge, Skills, and Abiliti...

引用

4th international conference on Advanced Research in Computing, ICARC 2024

作者： Chamithri, Kosala Asanka, P.P.G Dinesh University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798350384864

This research aims to develop a natural language processing (NLP) framework to uncover the current demanded knowledge, Skills, and Abilities (KSAs) in the Sri Lankan Information Technology (IT) sector. In the KSA, this paper focuses on professional qualifications and concepts, technical skills, and soft skills. Understanding the specific knowledge, Skills, and Abilities demanded by industries is not only relevant to academics but also holds practical significance for job seekers, educational institutions, and employers. By employing natural language processing techniques to analyze job advertisements, this research aims to bridge the skills gap, and support the relevant beneficiaries. The outcomes include the ability to use Generative AI for job advertisement annotation, an NLP framework to analyze job advertisements, identify the main areas in the IT sector according to ESCO (European Skills/Competences, Qualifications, and Occupations) classification-based customization and their specifically demanded KSAs for each domain. Importantly, the results also highlight that knowledge and Skills vary across different domains, while Abilities remain common with slight changes. © 2024 ieee.

关键词： natural language processing systems

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An NLP-Based Approach for Detecting Ambiguity of Thai Software Requirements Specification 21

An NLP-Based Approach for Detecting Ambiguity of Thai Softwa...

引用

21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Intana, Adisak Laosen, Kanjana Nuanchan, Panya Pattanakit, Nattapong Dermchai, Sathani Prince Songkla Univ Coll Comp Phuket Thailand

ISBN: (纸本)9798350381771;9798350381764

Writing software requirements in the form of natural language especially Thai language is very challenging. If software engineers do not have good writing skills, this may cause the ambiguity resulting in misunderstanding and misinterpretation during the development. To prevent this occurrence, this paper presents an NLP-based approach for detecting ambiguity of Thai software requirements. This approach influences an initiative fundamental of ambiguity detection mechanism at lexical level. The words potentially causing the ambiguity in software requirements are detected and classified into the ambiguity type. The contribution of the approach is demonstrated with the development of a prototype tool, Software Requirement Ambiguity Detector (SRAD). The validation and evaluation results with real software requirements from the various system domains with the practical expert perspective confirm the benefits of the proposed approach and developed tool. In the future, our tool and model will be integrated to our redesigned approach for writing Thai software requirements specification.

关键词： natural language requirement requirement engineering software requirements specifications ambiguity ambiguous software requirements natural language processing ambiguity detection

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from natural language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts natural language processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 ieee.

关键词： Semantics

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CiRA: An Open-Source Python Package for Automated Generation of Test Case Descriptions from natural language Requirements 31

CiRA: An Open-Source Python Package for Automated Generation...

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31st ieee international Requirements engineering conference (RE)

作者： Frattini, Julian Fischbach, Jamiik Bauer, Andreas Blekinge Inst Technol Karlskrona Sweden Netlight Consulting GmbH Munich Germany fortiss GmbH Munich Germany

ISBN: (纸本)9798350326918

Deriving acceptance tests from high-level, natural language requirements that achieve full coverage is a major manual challenge at the interface between requirements engineering and testing. Conditional requirements (e.g., "If A or B then C.") imply causal relationships which when extracted allow to generate these acceptance tests automatically. This paper presents a tool from the CiRA (Causality In Requirements Artifacts) initiative, which automatically processes conditional natural language requirements and generates a minimal set of test case descriptions achieving full coverage. We evaluate the tool on a publicly available data set of 61 requirements from the requirements specification of the German Corona-Warn-App. The tool infers the correct lest variables in 84.5% and correct variable configurations in 92.3% of all cases, which corroborates the feasibility of our approach.

关键词： requirements engineering natural language processing acceptance test test case description BERT

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