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cs.CL2026
When Modality Gap Reduction Fails: Prediction-Level Hubness in CLIP
Shota Sato, Hajime Kiyama, Tosho Hirasawa +1
Reducing the modality gap between image and text representations in CLIP is widely expected to improve cross-modal alignment and downstream performance. However, a smaller average…
cs.CL2026
Exploring the Effects of Alignment on Numerical Bias in Large Language Models
Ayako Sato, Hwichan Kim, Zhousi Chen +2
"LLM-as-a-judge," which utilizes large language models (LLMs) as evaluators, has proven effective in many evaluation tasks. However, evaluator LLMs exhibit numerical bias, a phenom…