Guide Labs debuts a new kind of interpretable LLM

Utilizing the Steerling-8B Model

Implement the Steerling-8B model, an 8 billion parameter LLM designed for interpretability. This model allows you to trace every token back to its origins in the training data, enhancing your understanding of the model's outputs and decisions.

Incorporating a Concept Layer

When building your LLM, integrate a concept layer that organizes data into traceable categories. This engineering approach simplifies the interpretability of the model, allowing for better control over its outputs.

Leveraging AI Models for Data Annotation

To efficiently implement the concept layer, utilize other AI models to assist in data annotation. This upfront investment in data preparation is crucial for creating a more interpretable LLM.

Tracking Discovered Concepts

Monitor the discovered concepts that the model identifies independently, such as its understanding of complex topics like quantum computing. This tracking ensures that the model retains its ability to generalize and learn beyond its training data.

Applying Interpretability Techniques in Regulated Industries

In regulated fields like finance and healthcare, apply the interpretability techniques from the Steerling-8B model to ensure compliance and ethical decision-making. For instance, when evaluating loan applicants, ensure the model considers relevant factors while avoiding biases related to race.