  Large Language Models have become the de facto approach to
sequence-to-sequence text generation tasks, but for specialized tasks/domains,
a pretrained LLM lacks specific capabilities to produce accurate or
well-formatted responses. Supervised fine-tuning specializes a LLM by training
it on dataset of example prompts with target responses, but real-world data
tends to be noisy. While many fine-tuning algorithms exist, here we consider a
\emph{data-centric AI} perspective on LLM fine-tuning, studying how to
\emph{systematically} curate the training dataset to improve the LLM produced
via \emph{any} fine-tuning algorithm.
  We introduce an automated data curation pipeline CLEAR (Confidence-based LLM
Evaluation And Rectification) for instruction tuning datasets, that can be used
with any LLM and fine-tuning procedure. CLEAR estimates which training data is
low-quality and either filters or corrects it. Automatically identifying which
data to filter or correct is done via LLM-derived confidence estimates, to
ensure only confident modifications to the dataset. Unlike existing data
curation techniques, CLEAR is a comprehensive framework that can improve a
dataset (and trained model outputs) without additional fine-tuning
computations. We don't assume access to a stronger LLM than the model being
fine-tuned (e.g.\ relying on GPT-4 when fine-tuning GPT-3.5), to see whether
CLEAR can meaningfully improve the capabilities of any LLM. Experiments reveal
that CLEAR consistently improves the performance of fine-tuned models across
many datasets and models (like GPT-3.5 and Llama2).
