LongForm: Effective Instruction Tuning with Reverse Instructions
CoRR(2023)
摘要
Instruction tuning enables language models to more effectively generalize and
better follow user intent. However, obtaining instruction data is costly and
challenging. Prior work employs methods such as expensive human annotation,
crowd-sourced datasets with alignment issues, and generating noisy examples via
LLMs. We introduce the LongForm-C dataset, which is created by reverse
instructions. We generate instructions via LLMs for human-written corpus
examples using reverse instructions. First we select a diverse set of
human-written documents from corpora such as C4 and Wikipedia; then we generate
instructions for these documents via LLMs. This approach provides a cheaper and
cleaner instruction-tuning dataset with natural output and one suitable for
long text generation. Our models outperform 10x larger language models without
instruction tuning on tasks such as story/recipe generation and long-form
question answering. Moreover, LongForm models outperform prior
instruction-tuned models such as FLAN-T5 and Alpaca by a large margin, and
improve language understanding capabilities further. Finally, our models can
effectively follow and answer multilingual instructions; we demonstrate this
for news generation. We publicly release our data and models:
https://github.com/akoksal/LongForm.
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关键词
longform text generation,instruction tuning,corpus extraction
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