5 citations · 6 across the 6 of their papers we have counts for
6 papers
cotomi Act: Learning to Automate Work by Watching You
Masafumi Oyamada, Kunihiro Takeoka, Kosuke Akimoto +5
What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines reliable multi-step task execu…
Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input. Compared to traditional methods such as collaborative filterin…
Are Longer Prompts Always Better? Prompt Selection in Large Language Models for Recommendation Systems
Genki Kusano, Kosuke Akimoto, Kunihiro Takeoka
In large language models (LLM)-based recommendation systems (LLM-RSs), accurately predicting user preferences by leveraging the general knowledge of LLMs is possible without requir…
Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models
Kosuke Akimoto, Taiki Miyagawa, Masafumi Oyamada
In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works adopt multi-epoch, multi-lingual,…
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization
Masafumi Enomoto, Kunihiro Takeoka, Kosuke Akimoto +2
Open-Domain Multi-Document Summarization (ODMDS) is the task of generating summaries from large document collections in response to user queries. This task is crucial for efficient…
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Kosuke Akimoto, Kunihiro Takeoka, Masafumi Oyamada
Retrieval-augmented generation models augment knowledge encoded in a language model by providing additional relevant external knowledge (context) during generation. Although it has…