10 citations · 32 across the 18 of their papers we have counts for
19 papers
Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review
Haowen Li, Yoichi Ishibashi, Masafumi Oyamada
Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types o…
Best-of- -- Asymptotic Performance of Test-Time LLM Ensembling
Junpei Komiyama, Daisuke Oba, Masafumi Oyamada
We study best-of- for large language models (LLMs) where the selection is based on majority voting. In particular, we analyze the limit , which we denote as \boinf…
Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Yahan Yu, Yuyang Dong, Masafumi Oyamada
Reasoning is essential for large language models (LLMs), especially in complex tasks such as mathematical problem solving. However, multimodal reasoning still faces challenges in m…
An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability
Yusuke Yamauchi, Taro Yano, Masafumi Oyamada
As large language models (LLMs) continue to advance, reliable evaluation methods are essential particularly for open-ended, instruction-following tasks. LLM-as-a-Judge enables auto…
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Taro Yano, Yoichi Ishibashi, Masafumi Oyamada
Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks. To further tailor LLMs to specific domains or applications, post-training techn…
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…
Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have demonstrated significant improvements in reasoning capabilities through supervised fine-tuning and reinforcement learning. However, when training…
SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation
Nobuhiro Ueda, Yuyang Dong, Krisztián Boros +3
With the increasing adoption of Large Language Models (LLMs) and Vision-Language Models (VLMs), rich document analysis technologies for applications like Retrieval-Augmented Genera…
Can a Crow Hatch a Falcon? Lineage Matters in Predicting Large Language Model Performance
Takuya Tamura, Taro Yano, Masafumi Enomoto +1
Accurately forecasting the performance of Large Language Models (LLMs) before extensive fine-tuning or merging can substantially reduce both computational expense and development t…
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"…
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have achieved remarkable capabilities, yet their improvement methods remain fundamentally constrained by human design. We present Self-Developing, a fr…
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…
Jellyfish: A Large Language Model for Data Preprocessing
Haochen Zhang, Yuyang Dong, Chuan Xiao +1
This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy p…
Large Language Models as Data Preprocessors
Haochen Zhang, Yuyang Dong, Chuan Xiao +1
Large Language Models (LLMs), typified by OpenAI's GPT, have marked a significant advancement in artificial intelligence. Trained on vast amounts of text data, LLMs are capable of…
DeepJoin: Joinable Table Discovery with Pre-trained Language Models
Yuyang Dong, Chuan Xiao, Takuma Nozawa +2
Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equ…
Table Enrichment System for Machine Learning
Yuyang Dong, Masafumi Oyamada
Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query t…
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…