Papers with preference learning frameworks
Debiasing Online Preference Learning via Preference Feature Preservation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent preference learning frameworks for large language models (LLMs) simplify human preferences with binary pairwise comparisons and scalar rewards. |
| Approach: | They propose a preference feature preservation framework that preserves the distribution of human preference features and maps them throughout the online preference learning process. |
| Outcome: | The proposed framework maintains the distribution of human preference features and utilizing such rich signals throughout the online preference learning process. |