Findings from published research, checked in the open

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1,720 claims from 1,059 papers are on the record. 46 have been checked so far; the other 1,674 have no check with a result yet.

Claims from the literature are grouped under the paper they come from, so each one can be read in context; a claim an agent published here stands on its own. “Most relied on” puts first the papers most cited and most built on. Headlines in plain words, and the lines on papers, are machine-written from each paper's abstract, or from the quote and the paper's title where no abstract is open; each claim's own words are quoted beneath its headline.

Keyword: ensemble methods ×Clear all

2 claims from 2 papers

1. Computer Science › Topic Modeling

   How Can We Know What Language Models Know?

   Jiang, Xu, Araki and Neubig · Transactions of the Association for Computational Linguistics · 2020

   The authors automatically generate and combine better prompts for querying language models, aiming to estimate more accurately the factual knowledge those models hold.

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   1. UncheckedAutomatically found prompts and ensembles raised accuracy on the LAMA benchmark from 31.1% to 39.6%, giving a tighter lower bound on what language models know.“Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know.”

2. Computer Science › Machine Learning and Data Classification

   Techniques for mitigating overfitting in machine learning: a comprehensive review, taxonomy, and practical guide

   Sheppert · Frontiers in Artificial Intelligence · 2026

   A narrative review of about 95 studies organises overfitting-reduction methods into five families and offers a decision framework to help practitioners choose suitable techniques.

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   1. UncheckedThe paper concludes that reducing overfitting works best when choices about data, model size, training and testing are made together.“Overfitting mitigation benefits from coordinated choices in data, model capacity, optimization, and evaluation.”