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Exploring How GPT-4 Can Explain Neurons’ Behavior in GPT-2 by OpenAI

Published on May 25, 2023

A work recently published by a team of OpenAI scholars has presented a fresh way to tackle the problem of current deep neural networks (DNNs) being difficult to interpret. By leveraging GPT-4, the researchers intend to construct a method to explain the circumstances that activate a neuron as the initial step towards automating DNN interpretability.

For OpenAI to comprehend Deep Neural Network (DNN) interpretability, three steps need to be accomplished: developing an explanation for how the neuron functions, simulating the neuron’s activation with the explanation, and computing a score for the explanation.

To initiate the process, a query is sent to the explainer model, resulting in an explanation of the neuron’s activation. For instance, this explanation may appear: “An understanding of neuron 1’s behaviour: this neuron detects phrases associated with the community”.

After obtaining an explanation, the subsequent action is to simulate the neuron’s behaviour based on the assumption that the explanation found is valid. This will generate a list of tokens and numbers between 0 and 10, indicating the likelihood of activation.

The third step entails obtaining a score for the explanation by evaluating the contrast between simulated and actual neuron behaviour. This is done by comparing the collection of tokens that was created during the simulation step to the output from the actual neuron for the same set of tokens. This step is the most complicated of the three and employs several algorithms that can produce different results.

OpenAI scientists have uncovered probable explanations for complex neurons, such as those associated with phrases that evoke feelings of trust. In addition, they have found another for correctly done activities. Even though the discoveries are still in the early stages, there remain a number of basic queries that need to be addressed. According to the researchers, these queries include whether neurons’ conduct can be justified.

Exploring DNN interpretability is an ongoing research effort to present an explanation of DNN performance that is comprehendible by a human being and linked to the domain in question. This topic is discussed in more detail here.

It is essential to have interpretability so that a human supervisor can comprehend if a DNN is performing as expected and can be trusted. This quality is essential when DNN failure may lead to disastrous results. Moreover, it can assist engineers in figuring out the main sources of DNN misbehaviour.

Interpretability has ethical and legal ramifications. Take, for example, European regulations that guarantee people the right to be exempt from algorithm decisions and to receive human intervention. This would be a difficult prospect if the individual responsible for making the decision could not understand the algorithm’s choice.

OpenAI’s detailed approach to interpreting DNNs is outlined in their article. It covers prompt examples, validation approaches, results, restrictions, and alternative evaluation algorithms. Anyone wishing to learn more about this approach should not miss it.

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