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ENRICHING MUSIC DESCRIPTIONS WITH A FINETUNED-LLM AND METADATA FOR TEXT-TO-MUSIC RETRIEVAL 49

ENRICHING MUSIC DESCRIPTIONS WITH A FINETUNED-LLM AND METADA...

引用

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

作者： Doh, SeungHeon Lee, TMinhee Jeong, Dasaem Nam, Vuhan Korea Adv Inst Sci & Technol Grad Sch Culture Technol Daejeon South Korea Sogang Univ Dept Comp Sci & Engn Seoul South Korea Sogang Univ Dept Art & Technol Seoul South Korea

ISBN: (纸本)9798350344868;9798350344851

Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of music audio and text, utilizing it to retrieve music tracks that exactly match descriptive queries related to musical attributes (i.e. genre, instrument) and contextual elements (i.e. mood, theme). However, users also articulate a need to explore music that shares similarities with their favorite tracks or artists, such as I need a similar track to Superstition by Stevie Wonder. To address these concerns, this paper proposes an improved Text-to-Music Retrieval model, denoted as TTMR++, which utilizes rich text descriptions generated with a finetuned large language model and metadata. To accomplish this, we obtained various types of seed text from several existing music tag and caption datasets and a knowledge graph dataset of artists and tracks. The experimental results show the effectiveness of TTMR++ in comparison to state-of-the-art music-text joint embedding models through a comprehensive evaluation involving various musical text queries. (1)

关键词： Music Informational Retrieval Text-to-Music Retrieval Large language Model

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Enhancing language Models Through Prompt engineering - A Survey 3

Enhancing Language Models Through Prompt Engineering - A Sur...

引用

3rd ieee international conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Padmaja, Chinimilli Venkata Rama Lakshminarayana, S. Institute of Aeronautical Engineering Computer Science & Engineering Hyderabad India Andhra University Computer Science & Systems Engineering Visakhapatnam India

ISBN: (纸本)9798350304329

In recent years, language models have undergone significant advancements with models like GPT-3, showcasing impressive abilities in natural language processing and generation. However, these models often experience from issues such as generating biased or unreliable content, producing inappropriate responses, or failing to fulfill specific user needs. To address these challenges, prompt engineering emerged as a promising approach by providing explicit guidance to language models. It refers to the process of designing and constructing effective prompts or instructions to guide language models like GPT-3 in generating desired outputs. In this article an attempt was made to address the role of prompt engineering, its techniques, and its potential impact on enhancing the performance and reliability of language models. © 2024 ieee.

关键词： ChatGPT language Models Prompt engineering

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Masked language Modeling for Resource Constrained Biological natural language processing

Masked Language Modeling for Resource Constrained Biological...

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45th Annual international conference of the ieee-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Bin Atif, Haasha Alvi, Hamza Naveed, Hammad Natl Univ Comp & Emerging Sci Islamabad Pakistan

ISBN: (纸本)9798350324471

Recent advances in natural language processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream tasks. Word vector models like Word2Vec, FastText & Glove were widely used over one-hot encoded vectors for years until the advent of deep contextualized embedders. Protein sequences consist of 20 naturally occurring amino acids that can be treated as the language of nature. These amino acids in combinations with each other makeup the biological functions. The choice of vector representation and architecture design for a biological task is highly dependent upon the nature of the task. We utilize unlabelled protein sequences to train a Convolution and Gated Recurrent Network (CGRN) embedder using Masked language Modeling (MLM) technique that shows significant performance boost under resource constraint setting on two downstream tasks i.e., F1-score(Q8) of 73.1% on Secondary Structure Prediction (SSP) & F1-score of 84% on Intrinsically Disordered Region Prediction (IDRP). We also compare different architectures on downstream tasks to show the impact of the nature of biological task on the performance of the model.

关键词： Amino acids

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Advancing Legal Decision Making: A Unique Method for Classifying Offense Incidents under the Indian Penal Code using Word Embedding and Labeled Searching

Advancing Legal Decision Making: A Unique Method for Classif...

引用

2024 international conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Srivastav, Ambrish Prajapat, Shaligram Acropolis Institute of Technology and Research Cse Department Indore India Iips Devi Ahilya Vishwavidyalya Cs Department Indore India

ISBN: (纸本)9798350388800

The advancements in artificial intelligence (AI) have propelled the domain of text similarity, where sophisticated algorithms harness the power of natural language processing to analyze and compare textual content. In the legal domain, the judicial system constitutes a vast landscape generating copious amounts of daily textual data, encompassing an array of documents such as court records, investigative reports, and First Information Reports (FIR). Moreover, a substantial amount of written content within this domain is found in legal articles and regulatory manuals. The similarity between these texts aids us in some way in making decisions. The Indian Penal Code (IPC) serves as the principal legislative framework in India for delineating criminal offenses. In the context of computer programs, maintaining information in a well-organized and structured format is crucial. This underscores the need for a suitable knowledge base that accurately represents the sections of the Indian Penal Code. Through evaluating various word embedding techniques and cosine similarity for measuring text similarity between crime complaint report and offense terms, this research reveals a method for categorizing IPC Sections. In addition, this study seeks to present a novel approach for creating a specialized knowledge base tailored to IPC documents. It also aims to introduce a labeled searching technique to augment the precision and efficacy of the acquired results. © 2024 ieee.

关键词： natural language processing systems

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Advancing Prompt Recovery in NLP: A Deep Dive into the Integration of Gemma-2b-it and Phi2 Models 6

Advancing Prompt Recovery in NLP: A Deep Dive into the Integ...

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6th ieee international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Chen, Jianlong Xu, Wei Ding, Zhicheng Xu, Jinxin Yan, Hao Zhang, Xinyu Beijing China Los Altos United States Renton United States Southern Methodist University Dallas United States Syracuse University Syracuse United States North Carolina State University Raleigh United States

ISBN: (纸本)9798350374315

Prompt recovery, a crucial task in natural language processing, entails the reconstruction of prompts or instructions that language models use to convert input text into a specific output. Although pivotal, the design and effectiveness of prompts represent a challenging and relatively untapped field within NLP research. This paper delves into an exhaustive investigation of prompt recovery methodologies, employing a spectrum of pre-trained language models and strategies. Our study is a comparative analysis aimed at gauging the efficacy of various models on a benchmark dataset, with the goal of pinpointing the most proficient approach for prompt recovery. Through meticulous experimentation and detailed analysis, we elucidate the outstanding performance of the Gemma-2b-it + Phi2 model + Pretrain. This model surpasses its counterparts, showcasing its exceptional capability in accurately reconstructing prompts for text transformation tasks. Our findings offer a significant contribution to the existing knowledge on prompt recovery, shedding light on the intricacies of prompt design and offering insightful perspectives for future innovations in text rewriting and the broader field of natural language processing. © 2024 ieee.

关键词： natural language processing systems

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Medical Prescription Generator using natural language processing 3

Medical Prescription Generator using Natural Language Proces...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paithankar, Siddhesh Patil, Snehal Shivgan, Ashlesha Mahabudhe, Mandar Dharmadhikari, Pramod MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of Electrical Engineering Pune India

ISBN: (纸本)9798331523893

With the increasing population in India and the limited availability of medical professionals, time efficiency in diagnosis and prescription generation has become crucial. This paper proposes an automated system leveraging natural language processing (NLP) and natural language Generation (NLG) to assist doctors in generating patient history and prescription suggestions. The system utilizes previously typed words to predict the next word, drawing on a medical notes dataset tailored to the doctor's specialization. By giving keywords or short descriptions about a patient's condition, the system processes the input using NLP for contextual under-standing and employs NLG to generate detailed histories and prescriptions in a standardized format. This dual approach ensures that the output aligns with medical conventions while maintaining accuracy and efficiency. The system is trained on extensive medical datasets, optimizing its predictions over time through machine learning techniques. Our solution not only enhances accuracy but also reduces the time doctors spend on manual documentation, improving overall efficiency and patient care. © 2025 ieee.

关键词： Diagnosis

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They use of NLP in Association with AI Technology for the Purpose of Optimization in Various Fields 4

They use of NLP in Association with AI Technology for the Pu...

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4th ieee international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Joshi, Ankita Bansal, Saloni Gard, Gopal Krishan Al-Farouni, Mohammed Srinivasan, S. Shetty, Surendra Uttaranchal University Uttaranchal Institute of Management Department of Management Dehradun248007 India Gla University Department of Computer Engineering and Applications Mathura India Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India The Islamic University Najaf Iraq Cmr College of Engineering & Technology Department of Eee India Department of Master of Computer Applications Nitte India

ISBN: (纸本)9798350360165

In the world of natural language processing (NLP), developing structures that not simplest carry out nicely but additionally offer apparent reasoning for their selections is paramount. Traditional gadget mastering fashions, in particular the ones based on deep gaining knowledge of, have excelled in diverse NLP duties. However, the ones models frequently act as 'black containers,' providing little to no perception into their selection-making processes. This opacity can undermine the don't forget and reliability perceived with the useful resource of customers. To deal with this trouble, we advocate a unique generative rationalization framework that endeavors to bridge the distance between high overall performance and explainability in NLP systems. This frameworks designed to concurrently make kind picks and generate unique, terrific-grained causes for the ones choices, integrating an explainable difficulty and minimum chance schooling. By providing human-readable justifications for its predictions, our framework enhances the interpretability and trustworthiness of NLP fashions. Experimental validation on two newly curated datasets demonstrates the framework's capacity to outperform existing baselines, significantly advancing the sector of explainable AI inside NLP. © 2024 ieee.

关键词： natural language processing systems

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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

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2024 ieee international conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, Vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru Veera Venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebaseVisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 ieee.

关键词： natural language processing systems

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Abstractive Text Summarization: A Transformer Based Approach 9

Abstractive Text Summarization: A Transformer Based Approach

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Kale, Anushka R. Deshmukh, Pratiksha R. Coep Technological University Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

This research delves into the difficulty of summarizing legal documents using natural language processing. It examines how cutting-edge models like XLNet and BART can be used for abstractive summarization specifically tailored for lengthy legal cases. The study assesses these models' abilities to condense complex legal texts, highlighting the constraints imposed by input token limits. Through a thorough comparison of XLNet and BART based on legal-specific standards, the research introduces a fresh approach to improve summarization by leveraging these models' strengths while addressing their limitations. Evaluation methods include ROUGE scores. This study advances our understanding of abstractive summarization, particularly in the realm of legal texts, offering valuable insights for both legal professionals and NLP researchers. © 2024 ieee.

关键词： natural language processing systems

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Fifth international conference on Control, Robotics, and Intelligent System, CCRIS 2024

Fifth International Conference on Control, Robotics, and Int...

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5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

ISBN: (纸本)9781510685864

The proceedings contain 39 papers. The topics discussed include: adaptive optimal output regulation: a parametric Lyapunov equation approach;evaluation on parameter-efficient continual instruction tuning of large language models;research on enhancing forecast accuracy of gold futures using LSTM neural networks across various time periods;FedGKD: personalized federated learning through grouping and distillation;the development of a question-answering system for learning article using natural language processing;KG-CQAM: knowledge graph and mind mapping-based complex question answering for large language models;and PCEM-SQL: enhancing text-to-SQL capabilities by large language models and prompt engineering with self-consistency and self-evaluation mechanism.

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