  Large language models (LLMs) have been significantly improved by instruction
fine-tuning, but still lack transparency and the ability to utilize up-to-date
knowledge and information. In this work, we propose search-augmented
instruction learning (SAIL), which grounds the language generation and
instruction following abilities on complex search results generated by in-house
and external search engines. With an instruction tuning corpus, we collect
search results for each training case from different search APIs and domains,
and construct a new search-grounded training set containing
\textit{(instruction, grounding information, response)} triplets. We then
fine-tune the LLaMA-7B model on the constructed training set. Since the
collected results contain unrelated and disputing languages, the model needs to
learn to ground on trustworthy search results, filter out distracting passages,
and generate the target response. The search result-denoising process entails
explicit trustworthy information selection and multi-hop reasoning, since the
retrieved passages might be informative but not contain the
instruction-following answer. Experiments show that the fine-tuned SAIL-7B
model has a strong instruction-following ability, and it performs significantly
better on transparency-sensitive tasks, including open-ended question answering
and fact checking.
