Capability Provenance in Language Models: A Case Study in Social Reasoning
We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social reasoning versus STEM reasoning in OLMo3-7B. Training-data attribution measures how strongly each training document influences a model's predictions on a benchmark, but document-level scores are too noisy to identify which corpus regions support which capabilities. We compute gradient-based attribution (TrackStar via Bergson) over a working set drawn from the de-duplicated Dolma3 mix, aggregate influence across WebOrganizer's 24-format x 24-topic taxonomy (576 bins), and contrast benchmark pairs in a 2x2 design that varies domain (social vs. ST...
| Authors | Glenn Matlin, Chandreyi Chakraborty, Saehee Eom et al. |
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| Primary category | cs.CL |
| Published | 17 June 2026 |
| Last updated at source | 5 October 2026 |
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