7,181 research outputs found
On the automated extraction of regression knowledge from databases
The advent of inexpensive, powerful computing systems, together with the increasing amount of available data, conforms one of the greatest challenges for next-century information science. Since it is apparent that much future analysis will be done automatically, a good deal of attention has been paid recently to the implementation of ideas and/or the adaptation of systems originally developed in machine learning and other computer science areas. This interest seems to stem from both the suspicion that traditional techniques are not well-suited for large-scale automation and the success of new algorithmic concepts in difficult optimization problems. In this paper, I discuss a number of issues concerning the automated extraction of regression knowledge from databases. By regression knowledge is meant quantitative knowledge about the relationship between a vector of predictors or independent variables (x) and a scalar response or dependent variable (y). A number of difficulties found in some well-known tools are pointed out, and a flexible framework avoiding many such difficulties is described and advocated. Basic features of a new tool pursuing this direction are reviewed
Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT and GPT-4,
state-of-the-art large language models (LLM) from the GPT series, and their
prospective applications across diverse domains. Indeed, key innovations such
as large-scale pre-training that captures knowledge across the entire world
wide web, instruction fine-tuning and Reinforcement Learning from Human
Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability
and performance. We performed an in-depth analysis of 194 relevant papers on
arXiv, encompassing trend analysis, word cloud representation, and distribution
analysis across various application domains. The findings reveal a significant
and increasing interest in ChatGPT/GPT-4 research, predominantly centered on
direct natural language processing applications, while also demonstrating
considerable potential in areas ranging from education and history to
mathematics, medicine, and physics. This study endeavors to furnish insights
into ChatGPT's capabilities, potential implications, ethical concerns, and
offer direction for future advancements in this field.Comment: 35 pages, 3 figure
Measuring Integrity for Selection into Medical School : Development of a Situational Judgement Test
This thesis examines the use of a Situational Judgement Test (SJT) for the selection into medical school based on the noncognitive attribute of integrity. Specifically, this thesis describes five studies that investigate how several test characteristics influence a number of quality criteria of an SJT in measuring integrity among medical school applicants
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