2 citations · 2 across the 8 of their papers we have counts for
14 papers
Identifying Influential N-grams in Confidence Calibration via Regression Analysis
Shintaro Ozaki, Wataru Hashimoto, Hidetaka Kamigaito +2
While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions demonstrating…
Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders
Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito
In real-world recommender systems, user-item interactions are Missing Not At Random (MNAR), as interactions with popular items are more frequently observed than those with less pop…
From Formal Language Theory to Statistical Learning: Finite Observability of Subregular Languages
Katsuhiko Hayashi, Hidetaka Kamigaito
We prove that all standard subregular language classes are linearly separable when represented by their deciding predicates. This establishes finite observability and guarantees le…
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws
Hidetaka Kamigaito, Ying Zhang, Jingun Kwon +3
Transformers deliver outstanding performance across a wide range of tasks and are now a dominant backbone architecture for large language models (LLMs). Their task-solving performa…
TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation
Shintaro Ozaki, Tomoyuki Jinno, Kazuki Hayashi +6
When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, the number of entities is almost c…
The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting
Masayo Tomita, Katsuhiko Hayashi, Tomoyuki Kaneko
Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications. While visual prompting is repor…