Guide Labs: Interpretable foundation models Features
Guide Labs develops interpretable foundation models that can reliably explain their reasoning, are easy to align and steer, and perform as well as standard black-box models.
View MoreKey Features of Guide Labs: Interpretable foundation models
Guide Labs offers interpretable foundation models (including LLMs, diffusion models, and classifiers) that provide explanations for their outputs, allow steering using human-understandable features, and identify influential parts of prompts and training data. These models maintain accuracy comparable to standard foundation models while offering enhanced transparency and control.
Explainable outputs: Models can explain and steer their outputs using human-understandable features
Prompt attribution: Identifies which parts of the input prompt most influenced the generated output
Data influence tracking: Pinpoints tokens in pre-training and fine-tuning data that most affected the model's output
Concept-level explanations: Explains model behavior using high-level concepts provided by domain experts
Fine-tuning capabilities: Allows customization with user data to insert high-level concepts for steering outputs
Use Cases of Guide Labs: Interpretable foundation models
Healthcare diagnostics: Provide explainable AI assistance for medical diagnoses while identifying influential factors
Financial decision-making: Offer transparent AI recommendations for lending or investment decisions with clear rationales
Legal document analysis: Analyze contracts or case law with explanations of key influential text and concepts
Content moderation: Flag problematic content with clear explanations of why it was flagged and what influenced the decision
Scientific research: Assist in hypothesis generation or data analysis with traceable influences from scientific literature
Pros
Maintains accuracy comparable to standard foundation models
Enhances transparency and interpretability of AI decisions
Allows for easier debugging and alignment of model outputs
Supports multi-modal data inputs
Cons
May require additional computational resources for explanations
Could be more complex to implement than standard black-box models
Potential trade-offs between interpretability and model performance in some cases
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