tuning as a function of number of tasks, model size, prompt settings, etc. Prompts used for instruction tuning
can be created by humans or by LLMs themselves (Zhou et al., 2022), and follow-up instructions can be used
to refine initial generations to make them more useful, engaging, and unbiased (Ganguli et al., 2023; Madaan
et al., 2023). An approach related to instruction tuning is chain-of-thought prompting (Wei et al., 2022b), in
which models are prompted to explain their reasoning when given a complex problem, in order to increase
the likelihood that their final answer is correct.
RLHF has emerged as a powerful strategy for fine-tuning Large Language Models, enabling significant
improvements in their performance (Christiano et al., 2017). The method, first showcased by Stiennon et al.
(2020) in the context of text-summarization tasks, has since been extended to a range of other applications.
Llama2