9 citations · 15 across the 9 of their papers we have counts for
9 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…
Are Large Language Models Really Effective for Training-Free Cold-Start Recommendation?
Genki Kusano, Kenya Abe, Kunihiro Takeoka
Recommender systems usually rely on large-scale interaction data to learn from users' past behaviors and make accurate predictions. However, real-world applications often face situ…
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…
LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries
Kenya Abe, Kunihiro Takeoka, Makoto P. Kato +1
Query expansion (QE) enhances retrieval by incorporating relevant terms, with large language models (LLMs) offering an effective alternative to traditional rule-based and statistic…
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed +11
Question Answering (QA) accounts for a significant portion of LLM usage "in the wild". However, LLMs sometimes produce false or misleading responses, also known as "hallucinations"…
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…
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…
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
Yuyang Dong, Kunihiro Takeoka, Chuan Xiao +1
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that…