HEIMAT debiases language models by generating heuristic prompts, building substitution sets, and fine-tuning the model with a Jensen-Shannon divergence loss to align predictions across demographic groups, with no fixed preference datasets.
P rom DA : Prompt-based Data Augmentation for Low-Resource NLU Tasks
1 Pith paper cite this work, alongside 65 external citations. Polarity classification is still indexing.
1
Pith paper citing it
65
external citations · OpenAlex
fields
cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Heuristic Perspective on Debiasing Language Models
HEIMAT debiases language models by generating heuristic prompts, building substitution sets, and fine-tuning the model with a Jensen-Shannon divergence loss to align predictions across demographic groups, with no fixed preference datasets.