A new 60-prompt benchmark shows most text-to-image models under-represent demographic groups, while LLM-guided diversification methods improve diversity without overcorrecting contextually specified attributes.
Flux.1: State-of-the-art image genera- tion.https://blackforestlabs.ai/, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Beyond Overcorrection: Evaluating Diversity in T2I Models with DivBench
A new 60-prompt benchmark shows most text-to-image models under-represent demographic groups, while LLM-guided diversification methods improve diversity without overcorrecting contextually specified attributes.