Appreciate it! > 1. did you post a summary of your chunking analysis somewhere? i'm curious which method maximized accuracy, and which sentence-overlap methods

Appreciate it! > 1. did you post a summary of your chunking analysis somewhere? i'm curious which method maximized accuracy, and which sentence-overlap methods were most effective. Good idea on this but nothing posted. In general, grouping by sections of a paper has worked best (i.e. methods, conclusions, results etc). GROBID is helpful with arbitrary papers. > 2. do you think general tokenization methods limit LLMs on scientific/biomedical papers? Possibly. For vectorization, specifically with medical, I do have this model (https://huggingface.co/NeuML/pubmedbert-base-embeddings https://huggingface.co/NeuML/pubmedbert-base-embeddings) which is a fine-tuned sentence embeddings model using this base model (https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-u...). The base model does have a custom vocabulary. In terms of LLMs, I've found that this model (https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) works well but haven't experimented with domain specific LLMs.