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AI Research
arXiv CS.AI·Jun 9·5 min read
Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units
TL;DR
Interpretable circuits in large language models (LLMs) lack clarity regarding their training data origins. Mechanistic Data Attribution (MDA) is introduced as a framework that uses Influence Functions to trace these circuits back to specific training samples.
✦ Why It Matters
Engineers can use MDA to enhance model interpretability and optimize training data selection.
Key Takeaways
- Implement Mechanistic Data Attribution for better model insights.
- Use Influence Functions to assess training sample impact.
- Experiment with targeted interventions on high-influence samples.
- Optimize training data selection based on causal origins.
Full Summary
Mechanistic Interpretability has made strides in identifying interpretable circuits within large language models (LLMs), but understanding their causal origins in training data remains a challenge. To address this, Mechanistic Data Attribution (MDA) was developed, utilizing Influence Functions—a technique that quantifies how much a training sample affects model predictions.
Extensive experiments were conducted on the Pythia family of models, demonstrating that targeted interventions, such as removing or augmenting a small fraction of high-influence samples, can significantly change the model's outputs. Results indicated that these modifications led to measurable shifts in model behavior, validating the causal link between specific training samples and interpretable units.
This work provides a scalable method for tracing the origins of model behavior, offering insights into the training data's role in shaping LLMs. The findings have implications for improving model interpretability and guiding data selection strategies for training.