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Related Work on Improving the Translation Agent #27
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From the paper and code provided by the author above, the approaches of these two projects are indeed very similar. |
The content of this project and this paper seems to be very similar. The paper is well developed and worthy of citation. |
Hi, Andrew and all collaborators on this project! It's great to see the implementation of such a fantastic concept bringing Agent into the Machine Translation field. In February, we also released an LLM-based Self-Refinement framework that aligns closely with your translation-agent project. We believe it may complement your work. For those interested, you can access our paper here: https://arxiv.org/abs/2402.16379 and the source code here: https://github.com/fzp0424/self_correct_mt. |
Andrew, i have a research paper on document similarity -- was wondering
what you'd think about it.
it's cryptocurrency document similarity--been working on it for 1+ year.
…On Mon, Jun 24, 2024 at 10:02 PM fzppp ***@***.***> wrote:
Hi, Andrew and all collaborators on this project! It's great to see the
implementation of such a fantastic concept bringing Agent into the Machine
Translation field. In February, we also released an LLM-based
Self-Refinement framework that aligns closely with your translation-agent
project. We believe it may complement your work. For those interested, you
can access our paper here: https://arxiv.org/abs/2402.16379 and the
source code here: https://github.com/fzp0424/self_correct_mt.
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@andrewyng @j-dominguez9 @methanet @johnsanterre I hope you find our work of interest. Thank you. |
Hello Andrew, thank you for your excellent contributions to the translation agent! I am reaching out to highlight our recent work, "TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement", which was released this February. Our research utilizes a self-refinement strategy along with a state-of-the-art GPT evaluation method to enhance translation agents, which may complement your work. For those interested, you can access our paper here: https://arxiv.org/abs/2402.16379 and the source code here: https://github.com/fzp0424/self_correct_mt.
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