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The Devil is in the Spectrum Bias: Spectrum-Balanced Feature Matching for Robust Representation Distillation
Kuniaki Saito, Yoshitaka Ushiku
Large visual foundation models have demonstrated remarkable transferability across a wide range of downstream tasks. To deploy such models efficiently, feature matching has become…
HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning
Kuniaki Saito, Risa Shinoda, Shohei Tanaka +3
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misreprese…
MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
Michiko Yoshitake, Yuta Suzuki, Ryo Igarashi +2
We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems th…
Am I More Pointwise or Pairwise? Revealing Position Bias in Rubric-Based LLM-as-a-Judge
Yuzheng Xu, Tosho Hirasawa, Tadashi Kozuno +1
Large language models are widely employed as evaluators, a paradigm commonly referred to as LLM-as-a-judge. Prior research has predominantly examined point-wise or pair-wise evalua…
WarrantScore: Modeling Warrants between Claims and Evidence for Substantiation Evaluation in Peer Reviews
Kiyotada Mori, Shohei Tanaka, Tosho Hirasawa +3
The scientific peer-review process is facing a shortage of human resources due to the rapid growth in the number of submitted papers. The use of language models to reduce the human…