Though the overwhelming majority of our explanations rating poorly, we imagine we are able to now use ML strategies to additional enhance our potential to supply explanations. For instance, we discovered we had been in a position to enhance scores by:
- Iterating on explanations. We are able to enhance scores by asking GPT-4 to provide you with doable counterexamples, then revising explanations in mild of their activations.
- Utilizing bigger fashions to offer explanations. The typical rating goes up because the explainer mannequin’s capabilities enhance. Nonetheless, even GPT-4 offers worse explanations than people, suggesting room for enchancment.
- Altering the structure of the defined mannequin. Coaching fashions with completely different activation capabilities improved rationalization scores.
We’re open-sourcing our datasets and visualization instruments for GPT-4-written explanations of all 307,200 neurons in GPT-2, in addition to code for rationalization and scoring using publicly available models on the OpenAI API. We hope the analysis neighborhood will develop new strategies for producing higher-scoring explanations and higher instruments for exploring GPT-2 utilizing explanations.
We discovered over 1,000 neurons with explanations that scored at the least 0.8, which means that in keeping with GPT-4 they account for a lot of the neuron’s top-activating conduct. Most of those well-explained neurons should not very fascinating. Nonetheless, we additionally discovered many fascinating neurons that GPT-4 did not perceive. We hope as explanations enhance we could possibly quickly uncover fascinating qualitative understanding of mannequin computations.