Position: An Inner Interpretability Framework for AI Inspired by Lessons
from Cognitive Neuroscience

Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

3 June 2024

Martina G. Vilas

Federico Adolfi

Papers citing "Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience"

10 / 10 papers shown

Title
Uncovering Intermediate Variables in Transformers using Circuit Probing Michael A. Lepori Thomas Serre Ellie Pavlick 49 7 0 07 Nov 2023
Characterizing Mechanisms for Factual Recall in Language Models Qinan Yu Jack Merullo Ellie Pavlick KELM 24 10 0 24 Oct 2023
Finding Neurons in a Haystack: Case Studies with Sparse Probing Wes Gurnee Neel Nanda Matthew Pauly Katherine Harvey Dmitrii Troitskii Dimitris Bertsimas MILM 153 170 0 02 May 2023
Dissecting Recall of Factual Associations in Auto-Regressive Language Models Mor Geva Jasmijn Bastings Katja Filippova Amir Globerson KELM 180 152 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 205 486 0 01 Nov 2022
The Debate Over Understanding in AI's Large Language Models Melanie Mitchell D. Krakauer ELM 64 196 0 14 Oct 2022
In-context Learning and Induction Heads Catherine Olsson Nelson Elhage Neel Nanda Nicholas Joseph Nova Dassarma ... Tom B. Brown Jack Clark Jared Kaplan Sam McCandlish C. Olah 232 453 0 24 Sep 2022
Toy Models of Superposition Nelson Elhage Tristan Hume Catherine Olsson Nicholas Schiefer T. Henighan ... Sam McCandlish Jared Kaplan Dario Amodei Martin Wattenberg C. Olah AAML MILM 117 183 0 21 Sep 2022
Natural Language Descriptions of Deep Visual Features Evan Hernandez Sarah Schwettmann David Bau Teona Bagashvili Antonio Torralba Jacob Andreas MILM 191 92 0 26 Jan 2022
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 219 2,098 0 28 Feb 2017