Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units — mentioned by strangecasts

Sat, 19 Sep 2026 00:04:54 GMT

> If you look at the field of mechanistic interpretability, compared to “GOFAI” like learned decision trees, an LLM is completely opaque. I think the field deserves more credit than that, there are plenty of interpretability tools like * natural language autoencoders for explanations of activations: https://transformer-circuits.pub/2026/nla/index.html (demo at https://www.neuronpedia.org/llama3.3-70b-it/nla ) * easier-to-interpret language model families like Backpack models: https://aclanthology.org/2023.acl-long.506/ * attribution graphs to trace internal reasoning steps…

Paper and discussion history. A mention is not an endorsement.

Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units · HackerPapers discussions

Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units — mentioned by strangecasts