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Found 16 papers, 4 papers with code
Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1
LLM's powerful language understanding and generation capabilities enable it to deeply explore complex problems in the field of humanities and social sciences, and provide innovative tools for academic research and practical applications.
Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models
We use methods similar to those in the field of functional neuroimaging analysis to locate and identify functional networks in LLM.
Analyzing Nobel Prize Literature with Large Language Models
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
A Survey of Foundation Models for Music Understanding
Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally.
Investigation of the effectiveness of applying ChatGPT in Dialogic Teaching Using Electroencephalography
However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test.
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks
To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset.
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.
