Forced CoT produces video-dependent reasoning chains but does not improve MCQ accuracy on Qwen2.5-VL with Video-MME and causes a small drop on the 7B variant.
Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
GPT-4o and Claude Sonnet 4 show similar susceptibility to bias on GSM8K (1.3% vs 1.2%) but differ sharply in acknowledgment rates (13% vs 75%) under a rubric-defined metric.
BiasIG is a multi-dimensional benchmark for social biases in T2I models that shows debiasing interventions frequently cause confounding discrimination effects.
A prefix-window mean-NLL memorization probe disagrees with full-span NLL and exact-recall in three cases on a controlled autoregressive testbed, leading to recommendations for multi-probe reporting.
citing papers explorer
-
Chains That See, Answers That Don't: A Multi-Aspect Evaluation Recipe for Forced Chain-of-Thought on Video-MME
Forced CoT produces video-dependent reasoning chains but does not improve MCQ accuracy on Qwen2.5-VL with Video-MME and causes a small drop on the 7B variant.
-
Beyond Accuracy: Measuring Bias Acknowledgment in Chain-of-Thought Reasoning for Responsible AI Evaluation
GPT-4o and Claude Sonnet 4 show similar susceptibility to bias on GSM8K (1.3% vs 1.2%) but differ sharply in acknowledgment rates (13% vs 75%) under a rubric-defined metric.
-
BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models
BiasIG is a multi-dimensional benchmark for social biases in T2I models that shows debiasing interventions frequently cause confounding discrimination effects.
-
Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed
A prefix-window mean-NLL memorization probe disagrees with full-span NLL and exact-recall in three cases on a controlled autoregressive testbed, leading to recommendations for multi-probe reporting.