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We introduce HTLM, a hyper-text language model trained on a large-scale webcrawl. Modeling hyper-text has a number of advantages: (1) it is easilygathered at scale, (2) it provides rich document-level and end-task-adjacentsupervision (e.g. class and id attributes often encode document categoryinformation), and (3) it allows for new structured prompting that follows theestablished semantics of HTML (e.g. to do zero-shot summarization by infillingtitle tags for a webpage that contains the input text). We show thatpretraining with a BART-style denoising loss directly on simplified HTMLprovides highly effective transfer for a wide range of end tasks andsupervision levels. HTLM matches or exceeds the performance of comparably sizedtext-only LMs for zero-shot prompting and fine-tuning for classificationbenchmarks, while also setting new state-of-the-art performance levels forzero-shot summarization. We also find that hyper-text prompts provide morevalue to HTLM, in terms of data efficiency, than plain text prompts do forexisting LMs, and that HTLM is highly effective at auto-prompting itself, bysimply generating the most likely hyper-text formatting for any availabletraining data. We will release all code and models to support future HTLMresearch.

HTLM: Hyper-Text Pre-Training and Prompting of Language Models