3 papers
cs.LG2026
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Jialu Wang, Heinrich Peters, Asad A. Butt +6
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training method…
cs.SE2025
CodeAlignBench: Assessing Code Generation Models on Developer-Preferred Code Adjustments
Forough Mehralian, Ryan Shar, James R. Rae +1
As large language models become increasingly capable of generating code, evaluating their performance remains a complex and evolving challenge. Existing benchmarks primarily focus…
cs.LG2023
Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation Program
Heinrich Peters, Alireza Hashemi, James Rae
Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence…