most citedLARCH: Large Language Model-based Automatic Readme Creation with Heuristics

6 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20232 cited

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…

cs.CL20236 cited

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…

cs.AI20232 cited

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…

cs.CL2023

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.…

cs.CL20232 cited

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…