Allen AI mined 351K real how-tos from the web – now your LLM instructions won’t suck anymore.
Every dev who’s chained an LLM to generate tutorials knows the pain: steps that skip basics, ignore safety, or straight-up invent tools. Allen Institute for AI (Ai2) just fixed that with How2Everything – a massive dataset to make your procedural LLMs actually useful.[1]
They scraped 351K real-world procedures from the web, then built How2Score: an LLM-as-judge that flags failures like missing prerequisites or unsafe steps. It agrees with humans 80.5% of the time – solid for eval scaling without endless annotators.[1]
This plugs right into your workflow: fine-tune instruction models on the dataset for better docs, onboarding bots, or repair guides. Evaluate RAG outputs pre-deploy. For agent builders, it’s procedural gold – train chains that output reliable step-by-step without the ‘consult a doctor’ cop-out every time.[1]
Vs. existing evals like AlpacaEval or MT-Bench, this targets how-to generation specifically with web-scale procedures. No more toy tasks; this is grounded in messy real-world instructions. Open-source drop means you beat proprietary tools to reliable ops agents.[1]
Grab it now: Dataset and How2Score on Hugging Face from Ai2. Fine-tune Llama-3.1-8B, benchmark your API calls, deploy to production guides. Pro tip: chain with Aletheia-style verification for bulletproof agents. Ready to ship how-to LLMs that don’t embarrass you?
Source: The Sequence Radar #807