Article may be outdated

This article is 60 days old. Some details may have changed since publication.

Hacker News·2 min read·hard

The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"

A
Anon84
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
AI Summary

Researchers have identified a phenomenon called the 'Reversal Curse,' where large language models trained on statements like 'A is B' fail to infer that 'B is A'. This study highlights a fundamental limitation in how current AI models process and generalize bidirectional relationships.

Why it matters

Understanding this limitation is crucial for improving the logical reasoning and knowledge retrieval capabilities of future AI systems.

Dive DeeperCreate a free account to unlock

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Owain Evans [ view email ] [v1] Thu, 21 Sep 2023 17:52:19 UTC (1,320 KB) [v2] Fri, 22 Sep 2023 18:08:20 UTC (1,319 KB) [v3] Thu, 4 Apr 2024 21:25:17 UTC (1,336 KB) [v4] Sun, 26 May 2024 17:45:21 UTC (1,336 KB) Full-text links: Access Paper: View a PDF of the paper titled The Reversal Curse: LLMs trained on A is B fail to learn B is A , by Lukas Berglund and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2023-09 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar 1 blog link ( what is this? ) export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Continue reading on Headlinne

Create a free account to read the full article.

Read full article →
technologyscience
Political Bias
Center
LeftLean LCenterLean RRight
Confidence: 90%

The content is a technical summary of an academic paper with no political or social bias.

Get smarter about the news

Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.

Create free account

Already have an account? Sign in