C2PO: Addressing Bias Shortcuts in LLMs
Analysis
This paper introduces C2PO, a novel framework to mitigate both stereotypical and structural biases in Large Language Models (LLMs). It addresses a critical problem in LLMs – the presence of biases that undermine trustworthiness. The paper's significance lies in its unified approach, tackling multiple types of biases simultaneously, unlike previous methods that often traded one bias for another. The use of causal counterfactual signals and a fairness-sensitive preference update mechanism is a key innovation.
Key Takeaways
- •C2PO is a unified alignment framework for mitigating both stereotypical and structural biases in LLMs.
- •It uses causal counterfactual signals to identify and suppress bias-inducing features.
- •The framework employs a fairness-sensitive preference update mechanism.
- •Experiments show C2PO effectively mitigates biases while preserving general reasoning capabilities.
“C2PO leverages causal counterfactual signals to isolate bias-inducing features from valid reasoning paths, and employs a fairness-sensitive preference update mechanism to dynamically evaluate logit-level contributions and suppress shortcut features.”