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Does Instruction Tuning Make LLMs More Consistent?
April 24, 2024, 4:47 a.m. | Constanza Fierro, Jiaang Li, Anders S{\o}gaard
cs.CL updates on arXiv.org arxiv.org
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 …
abstract alignment arxiv consistent cs.cl enabling impact language language models llms performance reasoning sensitivity small thought type value zero-shot
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