Does Instruction Tuning Make LLMs More Consistent?

Constanza Fierro; Jiaang Li; Anders Søgaard

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arXiv · 2404.15206

Does Instruction Tuning Make LLMs More Consistent?

Constanza Fierro·Jiaang Li·Anders Søgaard

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Abstract

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.

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Constanza Fierro, Jiaang Li, Anders Søgaard. 2024-04-23. Does Instruction Tuning Make LLMs More Consistent?. https://arxiv.org/abs/2404.15206

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