How can representation dimension dominate structurally pruned LLMs?

6 March 2025

Papers citing "How can representation dimension dominate structurally pruned LLMs?"

6 / 6 papers shown

Title
Round and Round We Go! What makes Rotary Positional Encodings useful? Federico Barbero Alex Vitvitskyi Christos Perivolaropoulos Razvan Pascanu Petar Velickovic 121 29 0 08 Oct 2024
Towards Automated Circuit Discovery for Mechanistic Interpretability Arthur Conmy Augustine N. Mavor-Parker Aengus Lynch Stefan Heimersheim Adrià Garriga-Alonso 66 319 0 28 Apr 2023
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Kevin Wang Alexandre Variengien Arthur Conmy Buck Shlegeris Jacob Steinhardt 316 563 0 01 Nov 2022
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 651 4,925 0 23 Jan 2020
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 277 3,489 0 09 Mar 2018
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 811 132,725 0 12 Jun 2017