402 citations · 923 across the 41 of their papers we have counts for
4 papers · 1 filter
The Sparsity Whisperer
Linghao Kong, Inimai Subramanian, Micah Adler +3
Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a…
Toy Combinatorial Interpretability Models Reveal Lottery Tickets in Early Feature Space
Alon Bebchuk, Nir Shavit
The lottery ticket hypothesis posits that dense networks contain sparse subnetworks, ``winning tickets,'' that, when rewound to their initial weights and retrained in isolation, ma…
An Interpretable Latency Model for Speculative Decoding in LLM Serving
Linghao Kong, Megan Flynn, Michael Peng +3
Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller draft model to propose multiple tokens that are verified by a larger target model in p…
Understanding Empirical Unlearning with Combinatorial Interpretability
Shingo Kodama, Niv Cohen, Micah Adler +1
While many recent methods aim to unlearn or remove knowledge from pretrained models, seemingly erased knowledge often persists and can be recovered in various ways. Because large f…