Table 2.

One-shot performance of the baseline models, original LLaMA, and instructed LLaMA on the medication status extraction and coreference resolution tasks.

	Medication status extraction				Coreference resolution
Models	Precision	Recall	F1	Conditional ACC	Precision	Recall	F1
PMC-LLaMA 7B	68.08	78	67.28 ± 0.3213	71.21 ± 0.3311	56.56	47.37	45.97 ± 0.1935
ChatDoctor	63.07	83.56	67.6 ± 0.0232	80.31 ± 0.3565	58.29	56.18	54.02 ± 0.0366
LLaMA 1 7B	67.61	71.95	66.58 ± 0.0601	78.45 ± 0.2048	60.14	39.19	43.8 ± 0.1914
LLaMA 1 7B Instruct	62.83	84.43	68.97 ± 0.241	82.52 ± 0.6844	67.58	52.52	55.14 ± 0.1765
LLaMA 2 7B	65.58	89.16	71.72 ± 0.2313	76.69 ± 0.1564	61.48	45.97	50.03 ± 0.3343
LLaMA 2 7B Instruct	75.44	82.83	75.63 ± 0.3701	82.3 ± 0.0664	72.58	58.69	61.24 ± 0.439

Conditional ACC measured the correctness of medication status classification, indicating how many statuses were correctly identified conditional on the extracted medications, P <.05; 95% confidence interval. Bold values indicate best performance.