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cs.LG2026
Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences
Cristina Garbacea
Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing for a hypothetical ``average use…
cs.AI2026
Personalized Benchmarking: Evaluating LLMs by Individual Preferences
Cristina Garbacea, Heran Wang, Chenhao Tan
With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences has become an important chal…