Data Distributional Properties Drive Emergent In-Context Learning in Transformers. - Stephanie C. Y. Chan; Adam Santoro; Andrew K. Lampinen; Jane X. Wang; Aaditya K. Singh; Pierre H. Richemond; James L. McClelland; Felix Hill (NeurIPS, 2022)

Know your audience: specializing grounded language models with the game of Dixit. - Aaditya K. Singh; David Ding; Andrew M. Saxe; Felix Hill; Andrew K. Lampinen (CoRR, 2022)

Know your audience: specializing grounded language models with listener subtraction. - Aaditya K. Singh; David Ding; Andrew M. Saxe; Felix Hill; Andrew K. Lampinen (EACL, 2023)

The Transient Nature of Emergent In-Context Learning in Transformers. - Aaditya K. Singh; Stephanie C. Y. Chan; Ted Moskovitz; Erin Grant; Andrew M. Saxe; Felix Hill (NeurIPS, 2023)

Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data. - Yu Yang; Aaditya K. Singh; Mostafa Elhoushi; Anas Mahmoud 002; Kushal Tirumala; Fabian Gloeckle; Baptiste Rozière; Carole-Jean Wu; Ari S. Morcos; Newsha Ardalani (CoRR, 2023)

Confronting Reward Model Overoptimization with Constrained RLHF. - Ted Moskovitz; Aaditya K. Singh; DJ Strouse; Tuomas Sandholm; Ruslan Salakhutdinov; Anca D. Dragan; Stephen Marcus McAleer (ICLR, 2024)

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; Stephanie C. Y. Chan; Andrew M. Saxe (ICML, 2024)

Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs. - Aaditya K. Singh; DJ Strouse (CoRR, 2024)

Quantifying Variance in Evaluation Benchmarks. - Lovish Madaan; Aaditya K. Singh; Rylan Schaeffer; Andrew Poulton; Sanmi Koyejo; Pontus Stenetorp; Sharan Narang; Dieuwke Hupkes (CoRR, 2024)

Brevity is the soul of wit: Pruning long files for code generation. - Aaditya K. Singh; Yu Yang; Kushal Tirumala; Mostafa Elhoushi; Ari S. Morcos (CoRR, 2024)

Evaluation data contamination in LLMs: how do we measure it and (when) does it matter? - Aaditya K. Singh; Muhammed Yusuf Kocyigit; Andrew Poulton; David Esiobu; Maria Lomeli; Gergely Szilvasy; Dieuwke Hupkes (CoRR, 2024)

The broader spectrum of in-context learning. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Aaditya K. Singh; Murray Shanahan (CoRR, 2024)

HARP: A challenging human-annotated math reasoning benchmark. - Albert S. Yue; Lovish Madaan; Ted Moskovitz; DJ Strouse; Aaditya K. Singh (CoRR, 2024)

Training Dynamics of In-Context Learning in Linear Attention. - Yedi Zhang; Aaditya K. Singh; Peter E. Latham; Andrew M. Saxe (CoRR, 2025)

Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks. - Rylan Schaeffer; Punit Singh Koura; Binh Tang; Ranjan Subramanian; Aaditya K. Singh; Todor Mihaylov; Prajjwal Bhargava; Lovish Madaan; Niladri S. Chatterji; Vedanuj Goswami; Sergey Edunov; Dieuwke Hupkes; et al. (CoRR, 2025)

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. - Aaditya K. Singh; Ted Moskovitz; Sara Dragutinovic; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (CoRR, 2025)

Distinct Computations Emerge From Compositional Curricula in In-Context Learning. - Jin Hwa Lee; Andrew K. Lampinen; Aaditya K. Singh; Andrew M. Saxe (CoRR, 2025)