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Adding a file for listing relevant research papers

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