The Geometry Of Truth: Emergent Linear Structure In LLM Representation Of True/False Datasets Deep Papers podcast

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The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets

11M ago 41:02

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For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss his paper, “The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets.” Samuel and his team curated high-quality datasets of true/false statements and used them to study in detail the structure of LLM representations of truth. Overall, they present evidence that language models linearly represent the truth or falsehood of factual statements and also introduce a novel technique, mass-mean probing, which generalizes better and is more causally implicated in model outputs than other probing techniques.
Find the transcript and read more here: https://arize.com/blog/the-geometry-of-truth-emergent-linear-structure-in-llm-representation-of-true-false-datasets-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

… continue reading

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#Science #Tech #Math #Business #Arize AI

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The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets

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published 11M ago

Delen

MP3•Thuis aflevering

Inhoud geleverd door Arize AI. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door Arize AI of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.

For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss his paper, “The Geometry of Truth: Emergent Linear Structure in LLM Representation of True/False Datasets.” Samuel and his team curated high-quality datasets of true/false statements and used them to study in detail the structure of LLM representations of truth. Overall, they present evidence that language models linearly represent the truth or falsehood of factual statements and also introduce a novel technique, mass-mean probing, which generalizes better and is more causally implicated in model outputs than other probing techniques.
Find the transcript and read more here: https://arize.com/blog/the-geometry-of-truth-emergent-linear-structure-in-llm-representation-of-true-false-datasets-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

… continue reading

35 afleveringen

#Science #Tech #Math #Business #Arize AI

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