no code implementations • WMT (EMNLP) 2021 • Kosuke Takahashi, Yoichi Ishibashi, Katsuhito Sudoh, Satoshi Nakamura
This paper describes our submission to the WMT2021 shared metrics task.
1 code implementation • 2 Apr 2024 • Yoichi Ishibashi, Yoshimasa Nishimura
To tackle this challenge, we propose Self-Organized multi-Agent framework (SoA), a novel multi-agent framework that enables the scalable and efficient generation and optimization of large-scale code.
Ranked #21 on Code Generation on HumanEval
1 code implementation • 21 Sep 2023 • Yoichi Ishibashi, Hidetoshi Shimodaira
We explore a knowledge sanitization approach to mitigate the privacy concerns associated with large language models (LLMs).
1 code implementation • 11 Feb 2023 • Yoichi Ishibashi, Danushka Bollegala, Katsuhito Sudoh, Satoshi Nakamura
To address this question, we conduct a systematic study of the robustness of discrete prompts by applying carefully designed perturbations into an application using AutoPrompt and then measure their performance in two Natural Language Inference (NLI) datasets.
1 code implementation • 24 Oct 2022 • Yoichi Ishibashi, Sho Yokoi, Katsuhito Sudoh, Satoshi Nakamura
In the field of natural language processing (NLP), continuous vector representations are crucial for capturing the semantic meanings of individual words.
2 code implementations • ACL 2020 • Yoichi Ishibashi, Katsuhito Sudoh, Koichiro Yoshino, Satoshi Nakamura
For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male.
no code implementations • ICLR 2018 • Yoichi Ishibashi, Hisashi Miyamori
In this paper, we propose the Associative Conversation Model that generates visual information from textual information and uses it for generating sentences in order to utilize visual information in a dialogue system without image input.