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Articles by Sergey Feldman
Synthesizing scientific literature with retrieval-augmented language models
9 hours ago After a year of headlines claiming AI would replace millions of jobs, companies are quietly discovering something else instead. AI isn’t eliminating human work. It’s redistributing human judgment, away from routine tasks and into the narrow zones where ambiguity is high, mistakes are costly, and …
Synthesizing scientific literature with retrieval-augmented language models - Nature
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Abstract Scientific progress depends on the ability of researchers to synthesize the growing body of literature. Can large language models (LLMs) assist scientists in this task? Here we introduce OpenScholar, a specialized retrieval-augmented language model (LM)1 that answers scientific queries by identifying relevant passages from 45 million open-access papers and synthesizing citation-backed responses.
By Akari Asai, Jacqueline Jialu He, Rulin Shao, Weijia Shi, Amanpreet Singh Verified, Joseph Chang Verified, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’Arcy, David Wadden, Matt Latzke, Jena D. Hwang, Varsha Kishore, Minyang Tian, Pan Ji, Shengyan Liu, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Daniel S. Weld, Doug B. Downey, Wen-tau Yih, Pang Wei Koh, Hannaneh Hajishirzi
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Nature
Verified
ABNIRML: Analyzing the Behavior of Neural IR Models
[Submitted on 2 Nov 2020 ( v1 ), last revised 20 Jul 2023 (this version, v2)] Title:ABNIRML: Analyzing the Behavior of Neural IR Models Download a PDF of the paper titled ABNIRML: Analyzing the Behavior of Neural IR Models, by Sean MacAvaney and 4 other authors Download PDF Abstract: Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.
Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction
[Skip to Content Landing] Figure 1. Sex Bias in Clinical Studies Over Time Determined From Published Articles for Cardiovascular Diseases, Diabetes, Digestive Diseases, and Hepatitis (Types A, B, C, and E) An intercept-only linear model was fitted to sex bias values from before and during 1993 and subsequently in 5-year increments.
What Statistics Topics are Needed for Excelling at Data Science?
By Sergey Feldman, Data Cowboys. "Data scientist" is a vague new job and you never know what tools you'll need to succeed. Lots of stuff I do at work I have never done before, but grad school was as much about learning how to learn quickly & think mathematically, as it was about learning specific models & techniques. In general, I recommend that you are able to (a) think in math and (b) code those thoughts up. Everything else you can teach yourself on the spot.
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