LLM Interpretability

What is LLM Interpretability?

LLM interpretability involves creating clear, actionable explanations for how a language model transforms inputs (like prompts and context) into outputs. It addresses critical questions:

• What features mattered? - Identifying the inputs and tokens that influenced the result.
• Where did it happen? - Understanding the parts of the model that carried signals, such as layers or attention heads.
• How can we intervene? - Implementing changes through prompt edits, data curation, and weight adjustments to alter outcomes in a predictable manner.

Key characteristics include:

• Local vs. Global Reasoning: Local explains a single output; global explains broader patterns.
• Causal over Correlational: Effective explanations remain valid even after changes are made.
• Operational Notes: The focus is on verifiable improvement rather than storytelling.

Why This Matters Now

Incorporating LLMs affects user experience and compliance. Interpretability supports rapid debugging and enhances reliability:

• Debugging: Identifies the specific inputs that influenced outputs.
• Reliability: Predicts and protects against potential failures.
• Safety and Compliance: Offers decision traces useful for audits.
• Optimization: Focuses improvements where they are most impactful.

Practical Techniques You Can Use

Common techniques in LLM interpretability include:

• Attribution on Inputs: Techniques like gradient saliency highlight influential tokens.
• Attention and Logit Lens: Examines token attention and contribution to outcomes.
• Probing Classifiers: Light probes pinpoint latent features by training on activations.
• Activation Patching: Modifies activations to measure impact on outputs.
• Steering Vectors: Directs behavior by adjusting activation pathways.
• Sparse Feature Isolation: Uses autoencoders to uncover interpretable features.
• Prompt Management: Refines behaviors through prompt tweaking.

Choosing the Proper Method for Your Decision

Select the technique that best matches your scenario:

• Prompt Tuning: Start with token-level attribution and hypothesis testing.
• Fine-tuning: Use probes to confirm concept representation.
• Governance: Employ steering vectors for sensitive content.
• Tool-calling: Ensure reliability by examining how models decide to use tools.

Common Challenges and Pitfalls

Key challenges include:

• Correlation vs. Causation: Confirm causality through interventions.
• Probe Overfitting: Avoid over-reliance on strong probes by testing causal editing.
• Prompt Sensitivity: Re-test under varying conditions to ensure stability.
• Cherry-picking: Ensure explanations are evaluated across diverse scenarios.
• Compute Creep: Perform initial triage with lightweight analysis.
• Access Limits: Navigate restricted access by working with available data.

Measuring Success

Evaluate the success of interpretability methods through:

• Faithfulness Tests: Ensure interventions lead to the expected outputs.
• Predictive Power: Analyze features' ability to predict outcomes.
• Improvement Metrics: Track business and safety improvements post-intervention.
• Robustness: Observe stability across varied inputs.
• Coverage: Confirm explanations address a wide range of cases.

A Practical Workflow for Teams

1. Define the Decision: Clearly outline the change needed.
2. Collect Cases: Sample and analyze production logs.
3. Run Triage: Employ quick diagnostics on potential solutions.
4. Form Hypotheses: Develop and test short, testable statements.
5. Test Causally: Use activation patching and other methods to confirm hypotheses.
6. Act and Verify: Implement changes and re-evaluate outcomes.
7. Monitor and Harden: Establish guardrails and track ongoing performance.

Interpretability accelerates troubleshooting and enhances governance, ensuring more reliable and effective AI deployments.