ICLR: In-Context Learning of Representations (Paper Walkthrough)

📖Paper: arxiv.org/abs/...
👥Authors: Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana, Yongyi Yang, Maya Okawa, Kento Nishi, Martin Wattenberg, Hidenori Tanaka
🏫Institutes: Harvard University, NTT Research Inc., University of Michigan, Ann Arbor
LLMs Learn New Tricks In-Context!: A Graph-Tracing Adventure🐒
This research investigates how LLMs alter their internal representations of concepts based on in-context examples 🐾🍎, rather than solely relying on pre-training data. Previous work focused primarily on the influence of pre-training data on LLM representations 🦉🍞; this study explores the dynamic adaptation of these representations in response to novel contextual information 🦋🥑. The researchers use a novel "graph tracing" task 🦊📊 to analyze this adaptation.
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