Tracing Positional Bias in Financial Decision-Making: Mechanistic Insights from Qwen2.5

Large Language Models (LLMs) have quickly become an essential asset for financial services, supporting everything from asset allocation, investment screening and portfolio rebalancing to risk assessment and regulatory compliance checks. While their promise of scale, speed, and performance won over the industry, a subtle yet significant  trap lurks behind the surface: positional bias. 

These models, often trained on vast datasets, systematically favor options based on their order, prioritizing those featured first even when there are more accurate responses located further down the list. As a result, they can distort high-stake decisions and prove detrimental to the business. A model that overweights early-listed assets can skew portfolios. One that overlooks companies listed further down will suffer consequences, such as missed opportunities, and steer trade strategies off course. As for compliance and audit contexts, bias-driven inconsistencies can also trigger regulatory scrutiny by exposing flawed judgment and prejudiced output. 

In his research, Bhaskarjit Sarmah and his colleagues confronted the challenge heads-on, introducing the first finance-specific benchmark for detecting and measuring positional bias in LLMs. Going further, they applied mechanistic interpretability that pinpoints where bias originates within the architecture of Qwen2.5 models (1.5B-14B). 

What they discovered was that bias was not only pervasive, but scale-sensitive, and present even with nuanced or complex prompts. For a high-stake environment such as financial services, positional bias allows real-world risks to creep in by distorting decisions with effects on portfolios, risk profiles, and compliance.