Summary of Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability, by Jiri Hron et al.

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability

by Jiri Hron, Laura Culp, Gamaleldin Elsayed, Rosanne Liu, Ben Adlam, Maxwell Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith

First submitted to arxiv on: 14 Aug 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The research paper investigates the impact of increasing training budget on language model hallucinations, which refer to incorrect answers that appear verbatim in the training set. The study constructs a knowledge graph-based dataset to control the training data content and trains increasingly large language models. The findings show that larger models hallucinate less for a fixed dataset, but require more compute resources. The paper also explores how detector size affects the performance of hallucination detectors, revealing an inverse relationship between the scale of the language model and the detectability of its hallucinations.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This study looks at how making language models bigger can affect their mistakes. It finds that bigger models make fewer mistakes if they’re trained on a certain dataset, but it takes a lot more computer power to train them. The research also shows that finding these mistakes gets harder as the model gets bigger.