9 citations · 17 across the 10 of their papers we have counts for
10 papers
Softmax Linear: Transformers may learn to classify in-context by kernel gradient descent
Sara Dragutinović, Andrew M. Saxe, Aaditya K. Singh
The remarkable ability of transformers to learn new concepts solely by reading examples within the input prompt, termed in-context learning (ICL), is a crucial aspect of intelligen…
Distinct Computations Emerge From Compositional Curricula in In-Context Learning
Jin Hwa Lee, Andrew K. Lampinen, Aaditya K. Singh +1
In-context learning (ICL) research often considers learning a function in-context through a uniform sample of input-output pairs. Here, we investigate how presenting a compositiona…
Strategy Coopetition Explains the Emergence and Transience of In-Context Learning
Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic +3
In-context learning (ICL) is a powerful ability that emerges in transformer models, enabling them to learn from context without weight updates. Recent work has established emergent…
Training Dynamics of In-Context Learning in Linear Attention
Yedi Zhang, Aaditya K. Singh, Peter E. Latham +1
While attention-based models have demonstrated the remarkable ability of in-context learning (ICL), the theoretical understanding of how these models acquired this ability through…
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…
The Transient Nature of Emergent In-Context Learning in Transformers
Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz +3
Transformer neural networks can exhibit a surprising capacity for in-context learning (ICL) despite not being explicitly trained for it. Prior work has provided a deeper understand…
Confronting Reward Model Overoptimization with Constrained RLHF
Ted Moskovitz, Aaditya K. Singh, DJ Strouse +4
Large language models are typically aligned with human preferences by optimizing (RMs) fitted to human feedback. However, human preferences are multi-facet…