5 papers · 1 filter
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability
Adam Karvonen, Can Rager, Johnny Lin +12
Sparse autoencoders (SAEs) are a popular technique for interpreting language model activations, and there is extensive recent work on improving SAE effectiveness. However, most pri…
Sparse Autoencoders Do Not Find Canonical Units of Analysis
Patrick Leask, Bart Bussmann, Michael Pearce +5
A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse…
LLM Circuit Analyses Are Consistent Across Training and Scale
Curt Tigges, Michael Hanna, Qinan Yu +1
Most currently deployed large language models (LLMs) undergo continuous training or additional finetuning. By contrast, most research into LLMs' internal mechanisms focuses on mode…
Linear Representations of Sentiment in Large Language Models
Curt Tigges, Oskar John Hollinsworth, Atticus Geiger +1
Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs). In this study, we reveal tha…
Can Transformers Learn to Solve Problems Recursively?
Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman +2
Neural networks have in recent years shown promise for helping software engineers write programs and even formally verify them. While semantic information plays a crucial part in t…