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Sufficient Context with Hailey Joren - Weaviate Podcast #125!

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Manage episode 492136520 series 3524543
Content provided by Weaviate. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Weaviate or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Hailey Joren is a Ph.D. student at UCSD! Hailey and collaborators at Duke University and Google have recently published Sufficient Context: A New Lens on Retrieval Augmented Generation Systems in ICLR 2025! There are so many interesting nuggets to this work! Firstly, it really helped me understand the difference between *relevant* search results and sufficient context for answering the question. Armed with this lens of looking at retrieved context, Hailey and collaborators make all sorts of interesting observations about the current state of Hallucination. RAG unfortunately makes the models far less likely to hallucinate, and the existing RAG benchmarks unfortunately do not emphasize retrieval adaptation well enough -- indicated by LLMs outputting correct answers despite insufficient context 35-62% of the time! However, reason for optimism! Hailey and team develop an autorater that can detect insufficient context 93% of the time! There are all sorts of interesting ideas around this paper! I really hope you find the podcast useful!

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126 episodes

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Manage episode 492136520 series 3524543
Content provided by Weaviate. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Weaviate or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Hailey Joren is a Ph.D. student at UCSD! Hailey and collaborators at Duke University and Google have recently published Sufficient Context: A New Lens on Retrieval Augmented Generation Systems in ICLR 2025! There are so many interesting nuggets to this work! Firstly, it really helped me understand the difference between *relevant* search results and sufficient context for answering the question. Armed with this lens of looking at retrieved context, Hailey and collaborators make all sorts of interesting observations about the current state of Hallucination. RAG unfortunately makes the models far less likely to hallucinate, and the existing RAG benchmarks unfortunately do not emphasize retrieval adaptation well enough -- indicated by LLMs outputting correct answers despite insufficient context 35-62% of the time! However, reason for optimism! Hailey and team develop an autorater that can detect insufficient context 93% of the time! There are all sorts of interesting ideas around this paper! I really hope you find the podcast useful!

  continue reading

126 episodes

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