27 citations · 50 across the 7 of their papers we have counts for
6 papers · 1 filter
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya +3
Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables them to generate intermediate thinking t…
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
Alliot Nagle, Adway Girish, Marco Bondaschi +3
We formalize the problem of prompt compression for large language models (LLMs) and present a framework to unify token-level prompt compression methods which create hard prompts fo…
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
Ashok Vardhan Makkuva, Marco Bondaschi, Chanakya Ekbote +4
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in…
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish +4
Attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling…
Rare Gems: Finding Lottery Tickets at Initialization
Kartik Sreenivasan, Jy-yong Sohn, Liu Yang +6
Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frank…
Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle +2
The strong {\it lottery ticket hypothesis} (LTH) postulates that one can approximate any target neural network by only pruning the weights of a sufficiently over-parameterized rand…