Cover Image

A fine-tuned large language model for domain-specific with reinforcement learning

Large Language Models (LLMs) like GPT-3 and BERT have significantly shown advancement in natural language processing by providing robust tools for understanding and generating human languages. However, their broad but shallow knowledge across many domains often leads to less effective performance in...

Full description

Saved in:
Bibliographic Details
Main Authors: Ismail, Amelia Ritahani, Aminuddin, Amira Shazleen, Nurul, Afiqa, Zakaria, Noor Azura
Format: Proceeding Paper
Language:English
Published: 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/116732/1/A_Fine-Tuned_Large_Language_Model_for_Domain-Specific_with_Reinforcement_Learning.pdf
http://irep.iium.edu.my/116732/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.116732
record_format dspace
spelling my.iium.irep.1167322024-12-18T02:21:06Z http://irep.iium.edu.my/116732/ A fine-tuned large language model for domain-specific with reinforcement learning Ismail, Amelia Ritahani Aminuddin, Amira Shazleen Nurul, Afiqa Zakaria, Noor Azura QA75 Electronic computers. Computer science Large Language Models (LLMs) like GPT-3 and BERT have significantly shown advancement in natural language processing by providing robust tools for understanding and generating human languages. However, their broad but shallow knowledge across many domains often leads to less effective performance in domain-specific tasks, where detailed and special- ized knowledge is needed. To address this limitation, this paper investigates the effectiveness of fine-tuning LLMs for specific domains. The approach incorporates reinforcement learning to integrate user feedback, allowing the model to dynamically adjust and refine its responses. This will ensure the model adapts iteratively, improving communication and interaction with users. The fine-tuned model’s performance is evaluated using two domain-specific datasets—medical and dental. Evaluation metrics such as Levenshtein distance and cosine similarity are used to assess the textual accuracy and semantic relevance of the fine-tuned model. The results from the dental the medical datasets indicate a low level of textual differences and strong semantic alignment, respectively. These suggest that the fine- tuned model effectively processes and preserves the integrity of domain-specific content with the potential of fine-tuning LLMs to enhance their applicability in specific domains. 2024-10-14 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/116732/1/A_Fine-Tuned_Large_Language_Model_for_Domain-Specific_with_Reinforcement_Learning.pdf Ismail, Amelia Ritahani and Aminuddin, Amira Shazleen and Nurul, Afiqa and Zakaria, Noor Azura (2024) A fine-tuned large language model for domain-specific with reinforcement learning. In: 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 7 Agust 2024, IIUM, Kuala Lumpur.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Amelia Ritahani
Aminuddin, Amira Shazleen
Nurul, Afiqa
Zakaria, Noor Azura
A fine-tuned large language model for domain-specific with reinforcement learning
description Large Language Models (LLMs) like GPT-3 and BERT have significantly shown advancement in natural language processing by providing robust tools for understanding and generating human languages. However, their broad but shallow knowledge across many domains often leads to less effective performance in domain-specific tasks, where detailed and special- ized knowledge is needed. To address this limitation, this paper investigates the effectiveness of fine-tuning LLMs for specific domains. The approach incorporates reinforcement learning to integrate user feedback, allowing the model to dynamically adjust and refine its responses. This will ensure the model adapts iteratively, improving communication and interaction with users. The fine-tuned model’s performance is evaluated using two domain-specific datasets—medical and dental. Evaluation metrics such as Levenshtein distance and cosine similarity are used to assess the textual accuracy and semantic relevance of the fine-tuned model. The results from the dental the medical datasets indicate a low level of textual differences and strong semantic alignment, respectively. These suggest that the fine- tuned model effectively processes and preserves the integrity of domain-specific content with the potential of fine-tuning LLMs to enhance their applicability in specific domains.
format Proceeding Paper
author Ismail, Amelia Ritahani
Aminuddin, Amira Shazleen
Nurul, Afiqa
Zakaria, Noor Azura
author_facet Ismail, Amelia Ritahani
Aminuddin, Amira Shazleen
Nurul, Afiqa
Zakaria, Noor Azura
author_sort Ismail, Amelia Ritahani
title A fine-tuned large language model for domain-specific with reinforcement learning
title_short A fine-tuned large language model for domain-specific with reinforcement learning
title_full A fine-tuned large language model for domain-specific with reinforcement learning
title_fullStr A fine-tuned large language model for domain-specific with reinforcement learning
title_full_unstemmed A fine-tuned large language model for domain-specific with reinforcement learning
title_sort fine-tuned large language model for domain-specific with reinforcement learning
publishDate 2024
url http://irep.iium.edu.my/116732/1/A_Fine-Tuned_Large_Language_Model_for_Domain-Specific_with_Reinforcement_Learning.pdf
http://irep.iium.edu.my/116732/
_version_ 1818833733129601024
score 13.223943

Similar Items