343 citations · 365 across the 8 of their papers we have counts for
8 papers
Exploring the Robustness of Large Language Models for Solving Programming Problems
Atsushi Shirafuji, Yutaka Watanobe, Takumi Ito +4
Using large language models (LLMs) for source code has recently gained attention. LLMs, such as Transformer-based models like Codex and ChatGPT, have been shown to be highly capabl…
Engineering faster double-array Aho-Corasick automata
Shunsuke Kanda, Koichi Akabe, Yusuke Oda
Multiple pattern matching in strings is a fundamental problem in text processing applications such as regular expressions or tokenization. This paper studies efficient implementati…
Are Prompt-based Models Clueless?
Pride Kavumba, Ryo Takahashi, Yusuke Oda
Finetuning large pre-trained language models with a task-specific head has advanced the state-of-the-art on many natural language understanding benchmarks. However, models with a t…
Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi, Yusuke Oda, Alexandra Birch +5
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Lang…
Findings of the Second Workshop on Neural Machine Translation and Generation
Alexandra Birch, Andrew Finch, Minh-Thang Luong +2
This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computat…
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita, Yusuke Oda, Graham Neubig +3
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sent…
Neural Machine Translation via Binary Code Prediction
Yusuke Oda, Philip Arthur, Graham Neubig +2
In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…