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Acquiring high-quality data is often a significant challenge in trainingmachine learning (ML) models for tabular prediction, particularly inprivacy-sensitive and costly domains like medicine and finance. Providingnatural language instructions to large language models (LLMs) offers analternative solution. However, it is unclear how effectively instructionsleverage the knowledge in LLMs for solving tabular prediction problems. Toaddress this gap, we introduce TABLET, a benchmark of 20 diverse tabulardatasets annotated with instructions that vary in their phrasing, granularity,and technicality. Additionally, TABLET includes the instructions' logic andstructured modifications to the instructions. We find in-context instructionsincrease zero-shot F1 performance for Flan-T5 11b by 44% on average and 13% forChatGPT on TABLET. Also, we explore the limitations of using LLMs for tabularprediction in our benchmark by evaluating instruction faithfulness. We findLLMs often ignore instructions and fail to predict specific instancescorrectly, even with examples. Our analysis on TABLET shows that, whileinstructions help LLM performance, learning from instructions for tabular datarequires new capabilities.

TABLET: Learning From Instructions For Tabular Data