REVIEW 2 cited by
FineDeb: A Debiasing Framework for Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
As language models are increasingly included in human-facing machine learning tools, bias against demographic subgroups has gained attention. We propose FineDeb, a two-phase debiasing framework for language models that starts with contextual debiasing of embeddings learned by pretrained language models. The model is then fine-tuned on a language modeling objective. Our results show that FineDeb offers stronger debiasing in comparison to other methods which often result in models as biased as the original language model. Our framework is generalizable for demographics with multiple classes, and we demonstrate its effectiveness through extensive experiments and comparisons with state of the art techniques. We release our code and data on GitHub.
Forward citations
Cited by 2 Pith papers
-
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models
A new Chinese bias benchmark with 4,077 instances and five tasks indicates larger language models are less biased than smaller ones when bias is measured through understanding tasks.
-
KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models
An attention-alignment fine-tuning objective (KL, CE, and triplet losses) reduces some bias scores on BBQ and BOLD for Llama-3.2-3B, but not consistently across models and with notable accuracy drops.
Discussion (0). Sign in to comment.