Articles for category: AI Research

March 15, 2025

ikayaniaamirshahzad@gmail.com

A New Benchmark for Assessing Hallucination in Medical Large Language Models

[Submitted on 25 Dec 2024 (v1), last revised 13 Mar 2025 (this version, v3)] View a PDF of the paper titled MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models, by Kaiwen Zuo and 1 other authors View PDF HTML (experimental) Abstract:Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations — generating medically implausible or inaccurate information — presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs. Our methodology integrates expert-validated medical case scenarios with established medical databases

March 15, 2025

ikayaniaamirshahzad@gmail.com

[2411.19553] Analysis of High-dimensional Gaussian Labeled-unlabeled Mixture Model via Message-passing Algorithm

[Submitted on 29 Nov 2024 (v1), last revised 13 Mar 2025 (this version, v2)] View a PDF of the paper titled Analysis of High-dimensional Gaussian Labeled-unlabeled Mixture Model via Message-passing Algorithm, by Xiaosi Gu and Tomoyuki Obuchi View PDF HTML (experimental) Abstract:Semi-supervised learning (SSL) is a machine learning methodology that leverages unlabeled data in conjunction with a limited amount of labeled data. Although SSL has been applied in various applications and its effectiveness has been empirically demonstrated, it is still not fully understood when and why SSL performs well. Some existing theoretical studies have attempted to address this issue by

March 15, 2025

ikayaniaamirshahzad@gmail.com

Google Research launches new scientific research tool, AI co-scientist

Today Google is launching an AI co-scientist, a new AI system built on Gemini 2.0 designed to aid scientists in creating novel hypotheses and research plans. Researchers can specify a research goal — for example, to better understand the spread of a disease-causing microbe — using natural language, and the AI co-scientist will propose testable hypotheses, along with a summary of relevant published literature and a possible experimental approach. AI co-scientist is a collaborative tool to help experts gather research and refine their work — it’s not meant to automate the scientific process. We’re excited to see how researchers will

March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

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March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 15, 2025

ikayaniaamirshahzad@gmail.com

Reddit – Heart of the internet

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

March 15, 2025

ikayaniaamirshahzad@gmail.com

a metadata format for ML-ready datasets

Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use as features. So much time, in fact, that progress in the field of ML is hampered by a fundamental obstacle: the wide variety of data representations. ML datasets cover a broad range of content types, from text and structured data to images, audio, and video. Even within datasets that cover the same types of content, every dataset has a unique ad hoc arrangement of files