4 papers · 1 filter
Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
Helena Casademunt, Caden Juang, Adam Karvonen +3
Fine-tuning large language models (LLMs) can lead to unintended out-of-distribution generalization. Standard approaches to this problem rely on modifying training data, for example…
Robustly Improving LLM Fairness in Realistic Settings via Interpretability
Adam Karvonen, Samuel Marks
Large language models (LLMs) are increasingly deployed in high-stakes hiring applications, making decisions that directly impact people's careers and livelihoods. While prior studi…
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…
Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks
Adam Karvonen, Can Rager, Samuel Marks +1
Sparse Autoencoders (SAEs) are an interpretability technique aimed at decomposing neural network activations into interpretable units. However, a major bottleneck for SAE developme…