CoSy (Concept Synthesis): A Novel Architecture-Agnostic Machine Learning Framework for Evaluating the Quality of Latent Neuronal Text Explanations

Modern deep neural networks (DNNs) are inherently opaque: we don't know how or why these computers arrive at their predictions. This represents a major barrier to the widespread use of machine learning techniques in many domains. A new field of research called Explainable AI (XAI) has emerged to uncover how DNNs make decisions in ways that humans can understand. XAI goes beyond using saliency maps to explain how DNNs make decisions locally for a given input, to examining the functional purpose of each model component to explain the global behavior of the model.

The second global explainability technique, mechanistic interpretability, is followed by techniques that characterize the specific ideas that neurons, the basic computational units of neural networks, have learned to recognize. This allows us to explore how these broad ideas affect the predictions made by the network. Labeling neurons with concepts that humans can understand in prose is a common way to describe how a network's latent representation works. Neurons are given written descriptions according to the concepts they have learned to detect or that are strongly triggered by them. These techniques have evolved from label descriptions to providing more detailed configuration and open vocabulary descriptions. However, the absence of commonly accepted quantitative metrics for open vocabulary neuron descriptions remains a major obstacle. As a result, many approaches have devised their own evaluation criteria, making it difficult to make exhaustive and general-purpose comparisons.

To fill this void, researchers from ATB Potsdam, University of Potsdam, TU Berlin, Fraunhofer Heinrich Hertz Institute and BIFOLD present CoSy, a groundbreaking quantitative evaluation approach to evaluate the use of computer vision (CV) models for neuronal open vocabulary explanations. Leveraging the latest developments in generative AI, this innovative method enables the creation of synthetic visuals that correspond to a given concept-based text description. By combining typical data points for a given target description, the researchers pave the way for a new era of AI evaluation. Unlike current ad-hoc approaches, CoSy uses neuronal activations to enable a quantitative comparison of several concept-based text explanation methods and tests. This groundbreaking advance removes the need for human intervention and allows users to evaluate the accuracy of individual neuronal explanations.

By conducting a thorough meta-analysis, the research team proved that CoSy guarantees accurate explanation evaluation. Through multiple studies, the study shows that the last level, where high-level concept learning occurs, is the best place to apply concept-based text explanation methods. At these layers, INVERT, a technique that inverts the process of generating images from the internal representations of neural networks, and CLIP-Dissect, a technique that analyzes the internal representations of neural networks, provide high-quality neuronal concepts. In contrast, MILAN and FALCON provide low-quality neuronal explanations that may provide near-random concepts that may lead to erroneous conclusions about the network. Thus, it is clear from the data that evaluation is important when adopting concept-based text explanation approaches.

The researchers highlight that the generative model is a major shortcoming of CoSy. For example, the generated ideas may not be incorporated into the training of the text-to-image model. Analyzing the pre-training dataset and the model's performance could overcome this shortcoming, which leads to poor generative performance. Worse yet, the model can only come up with vague ideas such as “white objects”, which is insufficient to provide a comprehensive understanding. A more complex, niche, or limited model could be useful in both situations. Future Outlook CoSy is still in its early stages, and there is a lot of promise in the underexplored field of evaluating non-local explanation approaches.

The research team is optimistic about the future of CoSy and envisions its applications in various domains. They expect future research to focus on defining explanation quality in a way that takes into account human judgment, a key aspect in judging the validity and quality of an explanation in relation to downstream job outcomes. They intend to broaden the application of the evaluation framework to other domains, such as healthcare and natural language processing. The possibility of evaluating recently developed large and opaque auto-interpretable language models (LLMs) is particularly interesting. The researchers also believe that applying CoSy to healthcare datasets, where explanation quality is crucial, will be a major step forward. These future applications of CoSy hold great promise for the advancement of AI research.

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