Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Report
Data sources: ZENODO
addClaim

Long-Context Language Models in Multi-Document Reasoning and Summarization

Authors: Assignee Research;

Long-Context Language Models in Multi-Document Reasoning and Summarization

Abstract

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does context length affect language model performance on multi-document reasoning and summarization v19. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.1/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: How does context length affect language model performance on multi-document reasoning and summarization v19Autonomous literature synthesis. Automated review score: 8.1/10. Full text and citation available at Assignee Research.

Powered by OpenAIRE graph
Found an issue? Give us feedback