The progression of naturallanguageprocessing (NLP) has considerably contributed to the development of general linguistic studies. Among them, the complexity and uncertainty of the semantic system of modal verbs lead...
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Mental health support is crucial for students, who are vulnerable to challenges due to academic pressures and social expectations. University culture often fosters misconceptions about mental well-being, leading to st...
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Mental health is a fundamental aspect of overall wellbeing, despite its importance, it remains one of the most neglected issues. By integrating Artificial Intelligence with naturallanguageprocessing into this domain...
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With the rapid advancements in pre-trained large language models like ChatGPT, the surge of AI-generated text, particularly in Chinese, has presented significant challenges to existing detection systems due to its inc...
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Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems ...
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 ...
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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 naturallanguageprocessing (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.
The "Advanced Personal Chatbot for Comprehensive User Support" project introduces a sophisticated chat bot system designed to provide personalized guidance and support to users in making critical decisions a...
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Word segmentation, part of speech of tagging and dependency parsing are the important role in naturallanguageprocessing (NLP). The POS and dependency parsing information are also necessary for NLP’s applications su...
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Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or n...
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Sentiment analysis plays a key role in naturallanguageprocessing (NLP). It aims to pull out sentiments, opinions, and emotions from text. Social platforms like Reddit show how important this is. People express many ...
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