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View Code? Open in Web Editor NEW[ACL 2024] LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Home Page: https://arxiv.org/abs/2403.15042
License: MIT License
[ACL 2024] LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Home Page: https://arxiv.org/abs/2403.15042
License: MIT License
It seems that instruction_per_seed_task is not reported in the Experiment Setup section of your paper? I find in your code that it's four by default.
Thank you for open-sourcing this work . I'd like to try it on my own dataset. But I cannot find the complete running pipeline 'run_all.sh'. Is it missing?
Readme says:
6. cd into your experiment folder and run ./run_all.sh
where is the experiments folder?
There just provide code for GSM8K, Will the code on other datasets be provided?
What is the approximate time if provided, thx~
I recently read your paper and it is a great paper. Your research provides valuable insights into LLM-based data augmentation.
As I was reading your paper, I couldn't help but notice the parallels between your findings and the work AI2 and I published last year in EMNLP, titled "[Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation]." Our paper delves into the targeted data augmentation for MWP solving, which might complement and extend the discussions in your paper.
Therefore, I was wondering if you might consider acknowledging our work in your paper, as it could provide additional depth to the understanding and implications of your findings for the readers. I would be more than happy to discuss this further or provide any additional information you might need regarding my work.
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