The paper presents a method for generating diverse and complex instruction data for large language models using attributed grounding, achieving top performance on benchmarks with a large synthesized dataset.
The pursuit of diverse, complex, and large-scale instruction data is crucial
for automatically aligning large language models (LLMs). While there are
methods capable of generating synthetic instructions at scale, they either
suffer from limited grounding sources, leading to a narrow distribution, or
rely on trivial extensions that fail to produce meaningful trajectories in
terms of complexity. In contrast, instructions that benefit efficient alignment
are typically crafted with cognitive insights and grounded in real-world use
cases. In this paper, we synthesize such instructions using attributed
grounding, which involves 1) a top-down attribution process that grounds a
selective set of real instructions to situated users, and 2) a bottom-up
synthesis process that leverages web documents to first generate a situation,
then a meaningful instruction. This framework allows us to harvest diverse and
complex instructions at scale, utilizing the vast range of web documents.
Specifically, we construct a dataset of 1 million instructions, called
SynthQuestions, and demonstrate that models trained on it achieve leading
performance on several common benchmarks, with improvements that continually
scale with more web corpora. Data, models and codes will be available at
https://github.com/Ignoramus0817/SynthQuestions.