48 citations · 49 across the 4 of their papers we have counts for
4 papers
Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement
Wataru Hirota, Tomoki Taniguchi, Tomoko Ohkuma +6
Evaluating LLM-generated business ideas is often harder to scale than generating them. Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria…
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains
Chencheng Zhu, Kazutaka Shimada, Tomoki Taniguchi +1
Large language models (LLMs) demonstrate the ability to learn in-context, offering a potential solution for scientific information extraction, which often contends with challenges…
Unbiased Learning for the Causal Effect of Recommendation
Masahiro Sato, Sho Takemori, Janmajay Singh +1
Increasing users' positive interactions, such as purchases or clicks, is an important objective of recommender systems. Recommenders typically aim to select items that users will i…
Submodular Bandit Problem Under Multiple Constraints
Sho Takemori, Masahiro Sato, Takashi Sonoda +2
The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this prob…