BACKGROUND AND AIMS: Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (LLMs) is a potentially more accurate method. To assess LLMs' potential for cirrhosis cohort identification, we compared code-based versus LLM-based classification with chart review as a "gold standard." APPROACH AND RESULTS: We extracted and conducted a limited chart review of 3788 discharge summaries of cirrhosis admissions. We engineered zero-shot prompts using a Generative Pre-trained Transformer 4 to determine whether cirrhosis and its complications were active hospitalization problems. We calculated positive predictive values (PPVs) of LLM-based classification versus limited chart review and PPVs of code-based versus LLM-based classification as a "silver standard" in all 3788 summaries. Compared to gold standard chart review, code-based classification achieved PPVs of 82.2% for identifying cirrhosis, 41.7% for HE, 72.8% for ascites, 59.8% for gastrointestinal bleeding, and 48.8% for spontaneous bacterial peritonitis. Compared to the chart review, Generative Pre-trained Transformer 4 achieved 87.8%-98.8% accuracies for identifying cirrhosis and its complications. Using LLM as a silver standard, code-based classification achieved PPVs of 79.8% for identifying cirrhosis, 53.9% for HE, 55.3% for ascites, 67.6% for gastrointestinal bleeding, and 65.5% for spontaneous bacterial peritonitis. CONCLUSIONS: LLM-based classification was highly accurate versus manual chart review in identifying cirrhosis and its complications. This allowed us to assess the performance of code-based classification at scale using LLMs as a silver standard. These results suggest LLMs could augment or replace code-based cohort classification and raise questions regarding the necessity of chart review.