4 citations · 4 across the 13 of their papers we have counts for
13 papers
Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments
Adam Karvonen, Euan Ong, Subhash Kantamneni +1
Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors. But what constitutes a "good" explanation? In…
Building Better Activation Oracles
Jan Bauer, Celeste De Schamphelaere, Adam Karvonen +2
Activation Oracles (AOs) are promising methods for interpreting residual stream activations. However, current AOs face important issues, such as hallucinations and vagueness. Addit…
Negation Neglect: When models fail to learn negations in training
Harry Mayne, Lev McKinney, Jan Dubiński +3
We introduce Negation Neglect, where finetuning LLMs on documents that flag a claim as false makes them believe the claim is true. For example, models are finetuned on documents th…
Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers
Adam Karvonen, James Chua, Clément Dumas +8
Large language model (LLM) activations are notoriously difficult to understand, with most existing techniques using complex, specialized methods for interpreting them. Recent work…
Automatically Finding Rule-Based Neurons in OthelloGPT
Aditya Singh, Zihang Wen, Srujananjali Medicherla +2
OthelloGPT, a transformer trained to predict valid moves in Othello, provides an ideal testbed for interpretability research. The model is complex enough to exhibit rich computatio…
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…
Revisiting End-To-End Sparse Autoencoder Training: A Short Finetune Is All You Need
Adam Karvonen
Sparse autoencoders (SAEs) are widely used for interpreting language model activations. A key evaluation metric is the increase in cross-entropy loss between the original model log…
Learning Multi-Level Features with Matryoshka Sparse Autoencoders
Bart Bussmann, Noa Nabeshima, Adam Karvonen +1
Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting neural networks by extracting the concepts represented in their activations. However, choosing the size…
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…
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Adam Karvonen, Benjamin Wright, Can Rager +6
What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representati…
Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models
Adam Karvonen
Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface lev…