AAAI Conference 2026 Short Paper
When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student Abstract)
- Ravada Satyadev
- Aditya Ganesh Kumar
- Avinash Anand
- Rajiv Ratn Shah
- Zhengkui Wang
- Mukesh Prasad
Bias in Large Language Models (LLMs) is increasingly addressed through fairness-oriented techniques. However, in some cases, these approaches may inadvertently remove genuine cultural differences between groups, leading to “over-normalization” or models losing important socio-cultural distinctions. In this work, we introduce OverNormEval, a benchmark designed to detect when an LLM exhibits such over-normalization. We further explore the use of Direct Preference Optimization (DPO) to mitigate over-normalization.