Example: add non-gradient attribution baselines - #366
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Two content-similarity baselines scored against a re-train bank and evaluated by per-query LDS, as a floor for the gradient methods: - activation similarity over the attribution target modules (uses the bank model's activations) - semantic search with jina-embeddings-v3 (pure text similarity) Point either at any bank written with save_retrained_models=true via --bank; the model and dataset are read from the bank's config.yaml, and LDS reuses evaluate_retrained's cached per-subset losses. Omit --bank to build the default GPT-2/WikiText bank first. On that bank they score mean per-query Spearman 0.13 and 0.09, below TrackStar (0.18), SOURCE (~0.39) and MAGIC (~0.92). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Two content-similarity LDS baselines:
jina-embeddings-v3.On the GPT-2 WikiText fine-tune re-train bank nicknamed
lotusthe mean LDSs are 0.129 and 0.095.