Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models

21 May 2024

Papers citing "Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models"

8 / 8 papers shown

Title
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms Michael Hanna Sandro Pezzelle Yonatan Belinkov 51 34 0 26 Mar 2024
Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT Zhengfu He Xuyang Ge Qiong Tang Tianxiang Sun Qinyuan Cheng Xipeng Qiu 32 20 0 19 Feb 2024
Attribution Patching Outperforms Automated Circuit Discovery Aaquib Syed Can Rager Arthur Conmy 57 54 0 16 Oct 2023
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model Michael Hanna Ollie Liu Alexandre Variengien LRM 186 116 0 30 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 210 491 0 01 Nov 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 240 456 0 24 Sep 2022
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 248 1,986 0 31 Dec 2020
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 226 4,453 0 23 Jan 2020