Aaditya K. Singh

According to our database, Aaditya K. Singh authored at least 17 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Distinct Computations Emerge From Compositional Curricula in In-Context Learning.

Jin Hwa Lee, Andrew K. Lampinen, Aaditya K. Singh, Andrew M. Saxe

CoRR, June, 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, March, 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, Sanmi Koyejo, Sharan Narang

CoRR, February, 2025

Training Dynamics of In-Context Learning in Linear Attention.

Yedi Zhang, Aaditya K. Singh, Peter E. Latham, Andrew M. Saxe

CoRR, January, 2025

2024

HARP: A challenging human-annotated math reasoning benchmark.

Albert S. Yue, Lovish Madaan, Ted Moskovitz, DJ Strouse, Aaditya K. Singh

CoRR, 2024

The broader spectrum of in-context learning.

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh, Murray Shanahan

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

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

Quantifying Variance in Evaluation Benchmarks.

Lovish Madaan, Aaditya K. Singh, Rylan Schaeffer, Andrew Poulton, Sanmi Koyejo, Pontus Stenetorp, Sharan Narang, Dieuwke Hupkes

CoRR, 2024

Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs.

Aaditya K. Singh, DJ Strouse

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Confronting Reward Model Overoptimization with Constrained RLHF.

Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen Marcus McAleer

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data.

Yu Yang, Aaditya K. Singh, Mostafa Elhoushi, Anas Mahmoud, Kushal Tirumala, Fabian Gloeckle, Baptiste Rozière, Carole-Jean Wu, Ari S. Morcos, Newsha Ardalani

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Know your audience: specializing grounded language models with listener subtraction.

Aaditya K. Singh, David Ding, Andrew M. Saxe, Felix Hill, Andrew K. Lampinen

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

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

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022