9 citations · 17 across the 10 of their papers we have counts for
4 papers · 1 filter
HARP: A challenging human-annotated math reasoning benchmark
Albert S. Yue, Lovish Madaan, Ted Moskovitz +2
Math reasoning is becoming an ever increasing area of focus as we scale large language models. However, even the previously-toughest evals like MATH are now close to saturated by f…
The broader spectrum of in-context learning
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh +1
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervis…
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Aaditya K. Singh, Ted Moskovitz, Felix Hill +2
In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-co…
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs
Aaditya K. Singh, DJ Strouse
Tokenization, the division of input text into input tokens, is an often overlooked aspect of the large language model (LLM) pipeline and could be the source of useful or harmful in…