Love the research presented in this Paper and had questions

Just read your [*Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3*](https://arxiv.org/abs/2406.11380), G. Michel, E. V. Epure, R. Hennequin and C. Cerisara (NAACL 2025).

And I have to say I'm impressed, This has confirmed my previous suspicions with [poorly done way too small sample size tests](https://github.com/DrewThomasson/Auto-Scalable-Speaker-Attribution-dataset) to attempt to see how well llm's preformed at quotation attribution.

This has me wondering though, are there any plans from these findings to use SOTA llm's to generate much larger datasets to train the booknlp BERT models on? Potentially for multiple languages?

As that would be a very cheap solution to the Now seemingly on a permanent hiatus [Multilingual Booknlp](https://www.neh.gov/sites/default/files/inline-files/FOIA%2021-09%20Regents%20of%20the%20University%20of%20California%2C%20Berkeley.pdf)?

Once again thank you so much for these open source contributions, it's been extremely helpful to me.

-Drew Thomasson, Georgia State University