I’m currently working on a production task to perform small-shot fine-tuning of large pretrained models in a live production environment. Given that we plan to fine-tune directly from the data generated by the model, rather than using traditional external datasets, I urgently need to find relevant and up-to-date research papers and materials to guide our work.

The research needs are as follows:
1.Data extraction strategy: We are looking for ways to extract data from large models for fine-tuning. If you know about relevant research work or have a recommended paper, please share generously.
2.Small-sample fine-tuning practices: We are exploring the best practices for small-sample fine-tuning. Especially when it comes to fine-tuning with small data sets, I seek specific requirements and recommendations for data quality. For example, what is the ideal ratio between noisy and high-quality data in a dataset? This will help us fine-tune the model more effectively, avoid overfitting, and improve the generalization ability of the model in practical applications. While data augmentation can be useful in some cases, for the time being we would like to leave this path aside in order to focus on other aspects of data selection and processing.

Help:
1.Recommend research directions or papers that meet the above criteria.
2.If possible, please provide a link to the paper or DOI

*Thesis Requirements:
1.Publication date: Preferably 2023 and onwards.
2.Closely related to the actual production environment.
3.If possible, please provide a link to the paper, a DOI, or other means of access.

We take this project very seriously and believe that any form of help will greatly advance our work. We look forward to hearing from you and suggestions!

Thank you from the bottom of our hearts for your time and support!