1 citations · 1 across the 10 of their papers we have counts for
12 papers
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…
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…
AgroBench: Vision-Language Model Benchmark in Agriculture
Risa Shinoda, Nakamasa Inoue, Hirokatsu Kataoka +2
Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs)…
CaptionSmiths: Flexibly Controlling Language Pattern in Image Captioning
Kuniaki Saito, Donghyun Kim, Kwanyong Park +2
An image captioning model flexibly switching its language pattern, e.g., descriptiveness and length, should be useful since it can be applied to diverse applications. However, desp…
Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research
Kan Hatakeyama-Sato, Toshihiko Nishida, Kenta Kitamura +4
This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. It emphasizes the dual roles of these models: cogni…
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
Risa Shinoda, Kuniaki Saito, Shohei Tanaka +2
Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and g…
Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictors
Akihiro Fujii, Yoshitaka Ushiku, Koji Shimizu +2
Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics. Practical design must satisfy multiple properties and physical con…
Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction
Kuniaki Saito, Kihyuk Sohn, Chen-Yu Lee +1
Large language models require updates to remain up-to-date or adapt to new domains by fine-tuning them with new documents. One key is memorizing the latest information in a way tha…