NeMo-Inspector: A Visualization Tool for LLM Generation Analysis

TL;DR

NeMo-Inspector is an open-source visualization tool that streamlines the analysis and cleaning of synthetic datasets for large language models, improving data quality and model performance.

Contribution

The paper introduces NeMo-Inspector, a novel tool that simplifies synthetic dataset analysis and error correction for LLM training.

Findings

Reduced low-quality samples in GSM-Plus dataset from 46.99% to 19.51%.

Improved accuracy of LLMs on MATH and GSM8K datasets after data cleaning.

Demonstrated effectiveness in real-world synthetic data analysis cases.

Abstract

Adapting Large Language Models (LLMs) to novel tasks and enhancing their overall capabilities often requires large, high-quality training datasets. Synthetic data, generated at scale, serves a valuable alternative when real-world data is scarce or difficult to obtain. However, ensuring the quality of synthetic datasets is challenging, as developers must manually inspect and refine numerous samples to identify errors and areas for improvement. This process is time-consuming and requires specialized tools. We introduce NeMo-Inspector, an open-source tool designed to simplify the analysis of synthetic datasets with integrated inference capabilities. We demonstrate its effectiveness through two real-world cases. Analysis and cleaning of the synthetically generated GSM-Plus dataset with NeMo-Inspector led to a significant decrease in low-quality samples from 46.99% to 19.51%. The tool also helped identify and correct generation errors in OpenMath models, improving accuracy by 1.92% on the MATH dataset and by 4.17% on the GSM8K dataset for a Meta-Llama-3-8B model fine-tuned on synthetic data generated from Nemotron-4-340B.