6 citations · 18 across the 11 of their papers we have counts for
13 papers
Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
Koki Takeshita, Kenji Uebayashi, Hikaru Tomonari +6
Recent studies have proposed reinforcement learning with verifiable rewards (RLVR) methods that enable LLMs to generate high-quality RTL circuits from natural-language specificatio…
Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks
Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita +3
When applying LLMs to real-world enterprise operations, LLMs need to handle proprietary knowledge in small domains of specific operations (). A previous stu…
Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains
Yawen Xue, Masaya Tsunokake, Yuta Koreeda +3
Agentic large language models (LLMs) have become prominent for autonomously interacting with external environments and performing multi-step reasoning tasks. Most approaches levera…
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi +1
Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (A…
Text Retrieval with Multi-Stage Re-Ranking Models
Yuichi Sasazawa, Kenichi Yokote, Osamu Imaichi +1
The text retrieval is the task of retrieving similar documents to a search query, and it is important to improve retrieval accuracy while maintaining a certain level of retrieval s…
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
Yuta Koreeda, Terufumi Morishita, Osamu Imaichi +1
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatic…
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi +1
We study a synthetic corpus based approach for language models (LMs) to acquire logical deductive reasoning ability. The previous studies generated deduction examples using specifi…
How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese
Takuro Fujii, Koki Shibata, Atsuki Yamaguchi +2
This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist be…
How does the task complexity of masked pretraining objectives affect downstream performance?
Atsuki Yamaguchi, Hiroaki Ozaki, Terufumi Morishita +2
Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts.…
Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
Yuta Koreeda, Ken-ichi Yokote, Hiroaki Ozaki +3
This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setu…
Controlling keywords and their positions in text generation
Yuichi Sasazawa, Terufumi Morishita, Hiroaki Ozaki +2
One of the challenges in text generation is to control text generation as intended by the user. Previous studies proposed specifying the keywords that should be included in the gen…
An Interactive Greedy Approach to Group Sparsity in High Dimensions
Wei Qian, Wending Li, Yasuhiro Sogawa +3
Sparsity learning with known grouping structure has received considerable attention due to wide modern applications in high-dimensional data analysis. Although advantages of using…
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa +5
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typic…