Imagine you’re leaning on a powerful AI to help pick investments. If the order in which options are presented nudges the model to pick one over another, you’ve got a hidden bias creeping into high-stakes decisions. That’s the core idea explored in this work, which dives into how and where these positional biases arise inside open-source financial AI models.

Introduction: Why a tiny bias matters in finance

Large language models (LLMs) are increasingly shaping finance—from screening investments to rebalancing portfolios and assessing risk. A well-known quirk in many LLMs is positional bias: a systematic preference for options based on their order, with primacy bias (favoring early choices) and recency bias (favoring later ones). In everyday chatter, this might seem harmless, but in finance it can distort asset allocation, risk assessments, and compliance checks.

This study takes that familiar idea and asks a tougher question: in financial tasks, do these biases behave differently as models scale up or as prompts are designed in particular ways? And more importantly, where in the model do these biases originate? The researchers tackle these questions with a first-of-its-kind framework that not only detects and measures bias but also peels back the layers to reveal its mechanistic roots.

What the study aims to achieve (in plain language)Create a unified framework and a finance-focused benchmark that tests how LLMs make binary decisions (choose option A vs. option B) when the options are presented in different orders.See how bias changes with model size (from smaller to larger versions) and with different ways of prompting (how you ask the model, what role you assign it, and how you frame the task).Trace bias to specific parts of the model (which layers or attention components light up when bias appears) to understand the “how” behind the bias.Provide actionable insights for safer, more trustworthy use of LLMs in financial settings.The framework at a glance: detection plus interpretationDetection: The team uses a finance-authentic dataset and a suite of binary decision tasks to measure how often the model gravitates toward one option just because of its position in the prompt.Mechanistic interpretability: Instead of only saying “bias exists,” they map it to concrete parts of the model—specific layers and attention heads—so we know where to intervene.Cross-scale and prompt-sensitive analysis: They test multiple Qwen2.5-instruct models ranging from 1.5 billion to 14 billion parameters and vary how prompts are structured to see how bias shifts.What’s special about the dataset

The researchers built a novel, finance-authentic set of prompts that span diverse asset classes and investor risk profiles. The goal is to reflect real-world decision contexts, not toy tasks. This helps ensure that findings are relevant for actual financial workflows.

Key findings: what they discovered about positional bias in financeBias is pervasive: Across the spectrum of Qwen2.5 models and prompt styles, positional bias shows up in financial decision tasks. It’s not a quirk of a single model or a single prompt type.It’s scale-sensitive: The amount and nature of bias change with model size. In other words, bigger isn’t automatically better for fairness here; how bias manifests shifts as you move from smaller to larger models.Prompt design matters: Small changes in how the task is framed—such as role assignment or how constraints are ordered—can noticeably alter outcomes. This echoes a broad finding in LLM research: presentation and framing can shape decision behavior.Primacy and recency effects in finance: Early- and late-presented options don’t just bias general reasoning; in risk-laden investment contexts, primacy and recency effects reveal specific vulnerabilities. For some prompts, the model leans toward early options; for others, it prefers late ones, especially when the quality across options diverges.Mechanistic paths: By peering into the model’s internals, the researchers show where bias originates and how it propagates. They link bias to particular layers and attention heads, offering a concrete map of where intervention could be most effective.What mechanistic interpretability adds to the story

Traditionally, people notice that a model is biased but don’t know why. This work goes a step further by:

Locating bias within the model’s architecture rather than labeling it as a black-box nuisance.Demonstrating that bias can emerge from specific components that handle positional information or decision framing.Providing a pathway to generalizable interventions: if you know which parts light up during biased behavior, you can target those parts for training, prompting, or governance controls.Practical takeaways for developers, users, and organizationsAudit before deployment: Use a finance-focused bias benchmark to test LLMs on order effects in decision tasks before integrating them into live financial workflows.Don’t rely on “bigger is better” for fairness: Model size changes the bias landscape. Larger models may not eliminate positional bias and can even introduce new vulnerabilities in certain prompt contexts.Design prompts thoughtfully: Small changes in how you frame the task (whose role the model plays, how you order options, what constraints you impose) can tilt outcomes. Systematic prompt design and testing are essential.Map and mitigate with interpretability: Use mechanistic interpretability to identify where bias originates in the model and apply targeted mitigations (for example, adjusting training data, fine-tuning, or prompt engineering tuned to reduce reliance on positional cues).Build governance around AI in finance: Combine domain-specific auditing with model interpretability to establish standards for transparency, safety, and reliability in AI-assisted financial decision-making.Prepare for regulatory and risk implications: As AI systems participate in decision-making that affects markets and customers, having a transparent bias-detection and mitigation framework helps meet accountability and governance requirements.Conclusion: a blueprint for trustworthy AI in finance

Positional bias in LLMs isn’t just a curiosity—it’s a real, measurable force that can shape financial decisions. By combining a dedicated benchmark with mechanistic interpretability, this work provides a practical framework for diagnosing where bias comes from and how it spreads through model components. The upshot is clear: to deploy AI responsibly in finance, we need both robust testing that reflects real-world decision contexts and transparent, mechanism-level insights that guide targeted mitigations. With these tools, we can move closer to AI that assists rather than subtly sways our financial choices.

The post Tracing Positional Bias in Financial Decision-Making: Mechanistic Insights from Qwen2.5 appeared first on Jacob Robinson.