7 papers
-MoE: Generalizing Mixture of Experts to Infinite Experts
Shota Takashiro, Takeshi Kojima, Shohei Taniguchi +2
The Mixture of Experts (MoE) selects a few feed-forward networks (FFNs) per token, achieving an effective trade-off between computational cost and performance. In conventional MoE,…
Automated Refinement of Essay Scoring Rubrics for Language Models via Reflect-and-Revise
Keno Harada, Lui Yoshida, Takeshi Kojima +2
The performance of Large Language Models (LLMs) is highly sensitive to the prompts they are given. Drawing inspiration from the field of prompt optimization, this study investigate…
When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following
Keno Harada, Yudai Yamazaki, Masachika Taniguchi +4
As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To…
Dynamic Injection of Entity Knowledge into Dense Retrievers
Ikuya Yamada, Ryokan Ri, Takeshi Kojima +2
Dense retrievers often struggle with queries involving less-frequent entities due to their limited entity knowledge. We propose the Knowledgeable Passage Retriever (KPR), a BERT-ba…
Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties
Gouki Minegishi, Hiroki Furuta, Takeshi Kojima +2
Recent large-scale reasoning models have achieved state-of-the-art performance on challenging mathematical benchmarks, yet the internal mechanisms underlying their success remain p…
A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics
Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa +8
Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to han…