AlpacaEval

A self-synthesis method (and family of datasets) that elicits high-quality instructions directly from an aligned LLM using only its chat template - no seed prompts required.

Berkeley NEST's seven-way ranked preference dataset built from GPT-4 rankings over responses from a diverse model pool, used to train Starling.

Teknium's million-row aggregation of high-quality GPT-4-style synthetic instructions that became the de facto open SFT baseline of 2023-2024.

Allen AI's flagship open SFT mixture combining new persona-driven prompts with curated public data for post-training a frontier-quality instruct model.

Tsinghua / OpenBMB's large-scale multi-turn dialog dataset generated by two LLMs talking to each other across structured topic taxonomies.

OpenBMB's 64k-prompt preference dataset built with GPT-4 critiques across instruction-following, truthfulness, honesty, and helpfulness - the de facto open DPO baseline.

Microsoft's "Evol-Instruct" recipe - automatically rewriting simple instructions into harder, more diverse ones using an LLM evolver.

Introduces AlpacaEval 2 with length control, a fast LLM-as-a-Judge benchmark whose ranking correlates 0.98 with Chatbot Arena after removing verbosity bias.