Uploaded July 2025 | Updated September 2026, 6 hours ago
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!
Links:
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems - arxiv.org/pdf/2411.06037
Chapters
0:00 Welcome Hailey
1:40 Definition of Sufficient Context
4:47 Context Engineering
8:42 AutoRater
13:08 Self-Rated Confidence
23:45 RAG and Long Context LLMs
29:35 Search Relevance vs. Sufficient Context
35:00 Retrieval-Aware Fine-Tuning
44:05 Directions for the future of AI
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!
Links:
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems - arxiv.org/pdf/2411.06037
Chapters
0:00 Welcome Hailey
1:40 Definition of Sufficient Context
4:47 Context Engineering
8:42 AutoRater
13:08 Self-Rated Confidence
23:45 RAG and Long Context LLMs
29:35 Search Relevance vs. Sufficient Context
35:00 Retrieval-Aware Fine-Tuning
44:05 Directions for the future of AI
