Auditing Asset-Specific Preferences in Financial LLMs: Bitcoin Bias Identified and Manipulated

Jul 16, 2026

A new preprint audits nine leading large language models (LLMs) for asset-specific preferences and finds that Bitcoin's ranking among money-like instruments is highly dependent on the scenario presented, with models ranking it higher in crisis and autonomous-agent contexts. The study identifies a dominant internal feature in Gemma 3 that selectively represents Bitcoin; manipulating this feature can causally shift the model's portfolio allocation toward or away from Bitcoin by up to 5.2 percentage points. This demonstrates that internal model representations can be both audited and causally linked to real financial decisions.

Why it matters: This work provides a concrete method for auditing and influencing asset-specific preferences in financial LLMs, laying groundwork for transparency and accountability as such models are deployed in real-world financial decision-making.

Full story at: arXiv Computers and Society ↗