LearnLM: Improving Gemini for Learning
Today's generative AI systems are tuned to present information by default
rather than engage users in service of learning as a human tutor would. To
address the wide range of potential education use cases for these systems, we
reframe the challenge of injecting pedagogical behavior as one of
pedagogical instruction following, where training and evaluation
examples include system-level instructions describing the specific pedagogy
attributes present or desired in subsequent model turns. This framing avoids
committing our models to any particular definition of pedagogy, and instead
allows teachers or developers to specify desired model behavior. It also clears
a path to improving Gemini models for learning -- by enabling the addition of
our pedagogical data to post-training mixtures -- alongside their rapidly
expanding set of capabilities. Both represent important changes from our
initial tech report. We show how training with pedagogical instruction
following produces a LearnLM model (available on Google AI Studio) that is
preferred substantially by expert raters across a diverse set of learning
scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over
Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.
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LearnLM Team
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Abhinit Modi
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Aditya Srikanth Veerubhotla
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Aliya Rysbek
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Andrea Huber
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Brett Wiltshire
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Brian Veprek
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Daniel Gillick
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Daniel Kasenberg
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Derek Ahmed
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Irina Jurenka
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James Cohan
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Jennifer She
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Julia Wilkowski
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Kaiz Alarakyia
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Kevin McKee
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Lisa Wang
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Markus Kunesch
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Mike Schaekermann
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Miruna Pîslar
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Nikhil Joshi
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Parsa Mahmoudieh
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Paul Jhun
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Sara Wiltberger
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Shakir Mohamed
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Shashank Agarwal
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Shubham Milind Phal
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Sun Jae Lee
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Theofilos Strinopoulos
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Wei-Jen Ko
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Amy Wang
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Ankit Anand
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Avishkar Bhoopchand
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Dan Wild
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Divya Pandya
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Filip Bar
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Garth Graham
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Holger Winnemoeller
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Mahvish Nagda
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Prateek Kolhar
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Renee Schneider
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Shaojian Zhu
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Stephanie Chan
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Steve Yadlowsky
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Viknesh Sounderajah
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Yannis Assael
LearnLM: Improving Gemini for Learning
Today's generative AI systems are tuned to present information by default
rather than engage users in service of learning as a human tutor would. To
address the wide range of potential education use cases for these systems, we
reframe the challenge of injecting pedagogical behavior as one of
pedagogical instruction following, where training and evaluation
examples include system-level instructions describing the specific pedagogy
attributes present or desired in subsequent model turns. This framing avoids
committing our models to any particular definition of pedagogy, and instead
allows teachers or developers to specify desired model behavior. It also clears
a path to improving Gemini models for learning -- by enabling the addition of
our pedagogical data to post-training mixtures -- alongside their rapidly
expanding set of capabilities. Both represent important changes from our
initial tech report. We show how training with pedagogical instruction
following produces a LearnLM model (available on Google AI Studio) that is
preferred substantially by expert raters across a diverse set of learning
scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over
Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.
Pedagogical instruction following enhances generative AI systems for educational purposes, leading to a preferred model (LearnLM) across various learning scenarios.
pedagogical instruction following LearnLM Google AI Studio GPT-4o Claude 3.5 Gemini 1.5 Pro