Extending evaluation beyond HH-RLHF to broader preference datasets

Investigate the applicability and performance of the DEFT framework on extensive and complex preference datasets beyond HH-RLHF’s harmlessness and helpfulness domains to assess whether DEFT’s data filtering and distribution-guided fine-tuning generalize to broader preference taxonomies.

Background

The experimental evaluation in the paper primarily relies on HH-RLHF, which focuses on harmlessness and helpfulness preferences. While DEFT proves effective under this setting, broader preference domains could present different distributional characteristics and challenges.

The authors explicitly acknowledge that more extensive and complex preference datasets have not been explored, motivating future work to validate DEFT’s generality beyond HH-RLHF.