Does Instruction Tuning Make LLMs More Consistent?
<|reference_start|>Does Instruction Tuning Make LLMs More Consistent?: The purpose of instruction tuning is enabling zero-shot performance, but instruction tuning has also been shown to improve chain-of-thought reasoning and value alignment (Si et al., 2023). Here we consider the impact on $\textit{consistency}$, i.e., the sensitivity of language models to small perturbations in the input. We compare 10 instruction-tuned LLaMA models to the original LLaMA-7b model and show that almost across-the-board they become more consistent, both in terms of their representations and their predictions in zero-shot and downstream tasks. We explain these improvements through mechanistic analyses of factual recall.<|reference_end|>
@article{fierro2024does,
    title={Does Instruction Tuning Make LLMs More Consistent?},
    author={Constanza Fierro, Jiaang Li, Anders S{o}gaard},
    journal={arXiv preprint arXiv:2404.15206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15206},
    primaryClass={cs.CL}
}