Which mechanisms increase the interpretability of LLMs by indicating influential input data?

Attention mechanisms play a crucial role in increasing the interpretability of large language models (LLMs) because they provide a way to visualize and understand the contributions of different input tokens to the model's predictions. In essence, attention allows the model to focus on specific words or phrases in the input when generating an output, highlighting which parts of the input are most influential in determining the response.

By examining the attention weights that are assigned to each part of the input during processing, researchers and practitioners can gain insights into how the model makes decisions and which elements are driving those decisions. This capability is particularly valuable in natural language processing tasks, where understanding context and relationships between words is vital. Because attention mechanisms explicitly quantify the importance of different inputs, they serve as a powerful tool for interpreting model behavior.

In contrast, the other options do not inherently provide this level of interpretability. Recurrent neural networks, while effective for sequence data, don’t provide direct insights into which inputs influenced a specific output. Convolutional layers are primarily used for spatial data and lack mechanisms for direct interpretability in sequential tasks such as language. Activation functions, on the other hand, serve to introduce non-linearity in the model but do not offer any direct explanation of input influence or decision-making