Making Large Language Models Interpretable: Beyond BERTopic (Part 2)

In the first part of our series, we explored how the BERTopic package can enhance the interpretability of Large Language Models (LLMs). By using topic modeling and clustering, BERTopic allows us to transform seemingly black-box LLMs into more comprehensible systems, offering insights into what they're focusing on when generating outputs.

Building on that exploration, we delve into additional techniques in this post. These alternative methods extend our understanding and interpretability of LLMs, offering different angles to dissect and comprehend these powerful models.

1. Layerwise Relevance Propagation (LRP)

Originating from the domain of image classification, Layerwise Relevance Propagation is a method that has been adapted to work with text data as well. It backpropagates the output activations of the model to the input layer, thereby providing a measure of 'relevance' for each input feature. This relevance metric can be visualized, offering insights into which words or phrases were significant for the model's decision.

2. Attention Visualization

Transformer-based models, like GPT-3 or BERT, leverage attention mechanisms to understand input data. These mechanisms weight the importance of different parts of the input when generating an output. By visualizing these attention scores, we gain insight into which parts of the input the model was 'paying attention' to when making its predictions. Tools like BertViz make this process straightforward and illustrative.

3. LIME (Local Interpretable Model-agnostic Explanations)

Originally developed for tabular and image data, LIME offers a way to understand machine learning models by creating a simpler, interpretable model around individual predictions. This simple model captures the behaviour of the complex model but is straightforward enough for a human to understand. For text, it provides a measure of feature importance for each word or phrase in the context of a specific prediction.

4. SHAP (SHapley Additive exPlanations)

SHAP, much like LIME, provides a way to understand the impact of different features on the model's predictions. With SHAP, each feature gets a score that reflects its contribution to the change in the output. In the context of text, it allows us to see how much each word or phrase contributes to the prediction.

5. Text-based Counterfactual Explanations

This approach changes the smallest possible portion of a text input to alter the model's prediction, giving us a sense of what information is critical for the model's decision. This 'minimal' change that affects the output can highlight key elements that the model is relying on, thereby enhancing our understanding of its internal workings.

6. Model-specific Tools

Finally, it's worth noting that some model architectures have their specific interpretability tools. The `Captum` library for PyTorch, for instance, includes several features like Integrated Gradients, DeepLIFT, and Guided Backpropagation, which can help in deciphering the working of a model.

While each of these methods offers its unique perspective and advantages, it's important to remember that the choice of method depends on the specific LLM in use, the task at hand, and the nature of insights sought from the model.?

From the PIML package for model interpretability in machine learning to BERTopic and these alternative techniques for LLMs, the strides towards making these powerful models more interpretable continue. A combination of these methods will undoubtedly provide a robust framework for understanding and leveraging LLMs.

In the next part of our series, we'll go into more detail about how each of these techniques works with practical examples and code snippets. So stay tuned!