201 citations · 693 across the 61 of their papers we have counts for
67 papers
Short-Context Dominance: How Much Local Context Natural Language Actually Needs?
Vala Vakilian, Zimeng Wang, Ankit Singh Rawat +1
We investigate the short-context dominance hypothesis: that for most sequences, a small local prefix suffices to predict their next tokens. Using large language models as statistic…
Continuous Chain of Thought Enables Parallel Exploration and Reasoning
Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang +3
Modern language models generate chain-of-thought traces by autoregressively sampling tokens from a finite vocabulary. While this discrete sampling has achieved remarkable success,…
Gating is Weighting: Understanding Gated Linear Attention through In-context Learning
Yingcong Li, Davoud Ataee Tarzanagh, Ankit Singh Rawat +2
Linear attention methods offer a compelling alternative to softmax attention due to their efficiency in recurrent decoding. Recent research has focused on enhancing standard linear…
Universal Model Routing for Efficient LLM Inference
Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat +9
Model routing is a simple technique for reducing the inference cost of large language models (LLMs), wherein one maintains a pool of candidate LLMs, and learns to route each prompt…
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs
Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh +12
A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such…
A Statistical Framework for Data-dependent Retrieval-Augmented Models
Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
Modern ML systems increasingly augment input instances with additional relevant information to enhance final prediction. Despite growing interest in such retrieval-augmented models…