Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability
Researchers investigated the Knobe effect—a moral bias in intentionality judgments—in finetuned large language models (LLMs). Using Layer-Patching analysis on three open-weights models, they found that the bias is acquired during finetuning and can be localized to specific layers. Importantly, patching activations from the pretrained model into these critical layers eliminated the bias without retraining the entire model.
Why it matters: This work demonstrates that social biases in LLMs can be interpreted, localized, and mitigated through targeted interventions, suggesting a practical path to safer models without full retraining.
Full story at: arXiv Computation and Language ↗