Beyond the black box: interpretability of LLMs in finance
Mechanistic interpretability identifies and locates modifications in computational mechanisms
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Large language models offer transformative potential in finance, yet their opacity is a barrier to deployment in high-stakes, regulated settings. Hariom Tatsat and Ariye Shater apply mechanistic interpretability to surface, monitor and steer internal features in Gemma 2 and GPT OSS, examining bias, hallucination, sentiment and trading setups for auditability in
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