<p>In a new study, OpenAI's GPT-4 is used to automatically explain the behavior of neurons within large language models like GPT-2.</p>
<p>Our understanding of how language models work internally is still quite limited. Research in interpretability aims to gain additional insights by looking inside the models. Traditionally, understanding what individual components (neurons and attention heads) do required manual examination by humans. However, this process does not scale for neural networks with tens or hundreds of billions of parameters.</p>
<p>This study proposes an automated process that uses GPT-4 to generate and evaluate natural language explanations of neuron behavior and applies it to the neurons of another language model.</p>
<p>This work is part of OpenAI's approach to researching alignment: automating the research process itself. One promising aspect of this approach is that it scales with the advancement of AI. As future models become smarter and more useful as assistants, we will receive better explanations.</p>
<p>📝 Paper: <a href="https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html">https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html</a><br />
🧪 View neurons: <a href="https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html">https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html</a><br />
👨💻 Github: <a href="https://github.com/openai/automated-interpretability">https://github.com/openai/automated-interpretability</a><br />
🔗 Post: <a href="https://openai.com/research/language-models-can-explain-neurons-in-language-models">https://openai.com/research/language-models-can-explain-neurons-in-language-models</a></p>;
<p>#ai #gpt #interpretability #openai #neurons #alignment</p>
