Evaluating the Utilities of Large Language Models in Single-cell Data Analysis
Evaluating the Utilities of Large Language Models in Single-cell Data Analysis
scEval presents a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell Large Language Models (LLMs), and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell LLMs, and points to their constraints and avenues for future investigation.
