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Gareth Davidson
35 lines
2.0 KiB
Markdown
35 lines
2.0 KiB
Markdown
# Research
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This page lists research papers that are relevant to the project.
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## Automatically Generating Instruction Data for Training
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This line of work is about significantly reducing the need for manually
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annotated data for the purpose of training
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[instruction-aligned](https://openai.com/blog/instruction-following/) language
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models.
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### SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions [[ArXiv](https://arxiv.org/pdf/2212.10560.pdf)], [[Github](https://github.com/yizhongw/self-instruct)].
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> We introduce SELF-INSTRUCT, a framework for improving the
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> instruction-following capabilities of pretrained language models by
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> bootstrapping off its own generations. Our pipeline generates instruction,
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> input, and output samples from a language model, then prunes them before using
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> them to finetune the original model. Applying our method to vanilla GPT3, we
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> demonstrate a 33% absolute improvement over the original model on
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> SuperNaturalInstructions, on par with the performance of InstructGPT-0011,
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> which is trained with private user data and human annotations.
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### Tuning Language Models with (Almost) No Human Labor. [[ArXiv](https://arxiv.org/pdf/2212.09689.pdf)], [[Github](https://github.com/orhonovich/unnatural-instructions)].
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> In this work, we introduce Unnatural Instructions: a large dataset of creative
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> and diverse instructions, collected with virtually no human labor. We collect
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> 64,000 examples by prompting a language model with three seed examples of
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> instructions and eliciting a fourth. This set is then expanded by prompting
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> the model to rephrase each instruction, creating a total of approximately
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> 240,000 examples of instructions, inputs, and outputs. Experiments show that
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> despite containing a fair amount of noise, training on Unnatural Instructions
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> rivals the effectiveness of training on open-source manually-curated datasets,
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> surpassing the performance of models such as T0++ and Tk-Instruct across
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> various benchmarks.
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