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Rahul Chalamala

3 accepted papers

2025

LoLCATs: On Low-Rank Linearizing of Large Language Models

ICLR 2025poster

Recent works show we can linearize large language models (LLMs)—swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear attention—avoiding the expensive pretraining costs. However, linearizing LLMs often significantly degrades model quality, stil…

2024

RedPajama: an Open Dataset for Training Large Language Models

NeurIPS 2024spotlight

Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset cur…

2023

LeanDojo: Theorem Proving with Retrieval-Augmented Language Models

NeurIPS 2023oral

Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has created substantial barriers to research on machine le…

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