Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters
Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters explores A lightweight method to mitigate bias in LLM-based recommendation systems without retraining, preserving accuracy.. Commercial viability score: 7/10 in LLM Recommendations.
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