How do you personalize AI models? A popular school of thought in AI is to just dump all the data you need into pre-training or fine tuning. But that may be less efficient and less controllable than alternatives — using AI models as a reasoning engine against external data sources.
Kelvin Guu, Senior Staff Research Scientist at Google, joins Sarah and Elad this week to talk about retrieval, memory, training data attribution and model orchestration. At Google, he led some of the first efforts to leverage pre-trained LMs and neural retrievers, with >30 launches across multiple products. He has done some of the earliest work on retrieval-augmented language models (REALM) and training LLMs to follow instructions (FLAN).
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Show Links:
Kelvin Guu Website
Google Scholar
FLAN: Finetuned Language Models Are Zero-Shot Learners
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
ROME: Locating and Editing Factual Associations in GPT
Branch-Train-Merge: Scaling Expert Language Models with Unsupervised Domain Discovery
Large Language Models Struggle to Learn Long-Tail Knowledge
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Show Notes:
[1:44] - Kelvin’s background in math, statistics and natural language processing at Stanford
[3:24] - The questions driving the REALM Paper
[7:08] - Frameworks around retrieval augmentation & expert models
[10:16] - Why is modularity important
[11:36] - FLAN Paper and instruction following
[13:28] - Updating model weights in real time and other continuous learning methods
[15:08] - Simfluence Paper & explainability with large language models
[18:11] - ROME paper, “Model Surgery” exciting research areas
[19:51] - Personal opinions and thoughts on AI agents & research
[24:59] - How the human brain compares to AGI regarding memory and emotions
[28:08] - How models become more contextually available
[30:45] - Accessibility of models
[33:47] - Advice to future researchers
