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Circuit Tracing: A Step Closer to Understanding Large Language Models
- Transformer-based large language models (LLMs) have advanced significantly, but they remain opaque in terms of how they process tasks.
- Understanding LLMs involves tracing their internal logic, treating neurons as the basic computational unit.
- A 'circuit' in LLMs is defined as a sequence of feature activations and connections used to transform input into output.
- To trace feature activations, a replacement model using transcoders is employed in place of MLP blocks in transformer models.
- Cross-layer transcoders (CLT) capture effects across multiple layers, aiding in circuit tracing.
- An attribution graph is built from the replacement model, showing the computational path with feature interpretability.
- Researchers used attribution graphs to understand how models plan ahead in tasks like poem generation.
- While circuit tracing is a significant step towards interpretability, limitations remain in understanding global circuits and inactive features.
- This approach provides insight into how LLMs generate text, aiding in alignment, safety, and trust in AI systems.
- Circuit tracing marks a crucial milestone on the journey to achieving true interpretability in large language models.
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