A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical data analysis. We investigated the potential of the ChatGPT Advanced Data An
In this paper, we aim to systematically investigate the capabilities of GPT-4o in addressing 10 low-level data analysis tasks. Our study seeks to answer the following critical questions, shedding light on the potential of MLLMs in performing detailed, granular analyses. ...
ReadPaper是粤港澳大湾区数字经济研究院推出的专业论文阅读平台和学术交流社区,收录近2亿篇论文、近2.7亿位科研论文作者、近3万所高校及研究机构,包括nature、science、cell、pnas、pubmed、arxiv、acl、cvpr等知名期刊会议,涵盖了数学、物理、化学、材料、金融、计算机
Therefore, the referred paper assigns learnable parameters to the auxiliary information. The main purpose of compression is for efficiency, of course, but moreover, we expect that MoAI-Compressor corrects wrong information or eliminates non-relevant information for vision language tasks. Q2. Have you...
In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that ...
Q18:What did the recent paper identify as a new potential explanation of the problem concerning men's employment? Recording 2 音频原文 While an increasing number of people are trying to eat less meat, a market research team has...
This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a CoT ...
we refrain from fine-tuning the model's identity. Thus, inquiries such as "Who are you" or "Who developed you" may yield random responses that are not necessarily accurate. If you enjoy our model, please give it a star on our Hugging Face repo and kindly cite our model. Your support ...
Implementing early stopping (patience=3), the model generally completes training in just 4 or 5 epochs. This suggests convergence occurring by the first or second epoch.Q2: Why does validation seem sluggish? A2: The delay in validation is largely due to the caching of SPARQL executions. The...
The answer to these questions lies in scaling laws. Scaling laws determines how much optimal data is required to train a model of a particular size. In 2022, DeepMind proposed the scaling laws for training the LLMs with the optimal model size and dataset (no. of tokens) in the paperTraini...