N e M o-Inspector: A Visualization Tool for LLM Generation Analysis (2025)

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.

## introduction large language models (llms) have demonstrated remarkable capabilities across various applications. recently, instead of limited and difficultto-obtain real-world data, synthetic datasets have become widely popular. however, the quality of the synthetic data cannot be guaranteed. developers spend a lot of time analyzing model predictions and tuning the prompt to find areas for potential improvements. this labor-intensive process underscores the need for a tool that can streamline dataset examination, simplify prompt adjustment, and ultimately expedite the refinement of model behaviors. there are several tools available that offer valuable features for dataset analysis, but each has its own limitations. @xcite includes features for comprehensive data analysis, but lacks the convenience of sample-by-sample inspection and text formatting. lilac @xcite focuses on the analysis of llm datasets but does not support inference, latex formatting, or multi-generation comparisons. llm-attributor @xcite is a specialized tool for understanding the influence of training data on the generated text; however, it is not suitable for the analysis of independent generations. finally, llm-comparator @xcite allows side-by-side comparisons of model outputs but lacks integrated inference capabilities, manual editing, and handling for homogeneous multi-generation scenarios, allowing not only side-by-side but also cross-generation analysis. to fill this research gap, we introduce nemo-inspector foot_0 -an open-source tool released under an apache 2.0 license. nemo-inspector provides the following features: nemo-inspector facilitates systematic analysis of llm outputs by categorizing them into homogeneous and heterogeneous generations. homogeneous generations, which are produced by varying random seeds within the same model and parameter settings, can be grouped for collective analysis. this approach enables the computation of aggregate statistics, such as the proportion of correct responses. in contrast, heterogeneous generations, originating from distinct models, parameter configurations, or tasks, are inherently disparate and not directly comparable as a unified set. these outputs are examined individu- ## tool overview the tool includes two pages that can be used separately or together: the inference page, which focuses on interactive prompt design, and the analyze page, which provides a comprehensive examination of the model's outputs. by integrating both inference and llm generation analysis within a single tool, users can efficiently test hypotheses and gain insights from generation analysis in real time. ## usage scenarios most of the following applications are based on mathematical datasets, as these were the primary focus of our work. however, the tool is not restricted to mathematical benchmarks and can be effectively applied to any other text datasets. the only requirement is that the datasets be in json lines format. ## conclusion this paper introduces nemo-inspector, an open source tool developed to facilitate the analysis of llm generations and experimentation with various inference configurations. it offers simultaneous analysis from different perspectives by allowing to work with both homogeneous and heterogeneous llm generations. nemo-inspector features advanced visualization capabilities and provides streamlined inference, as well as user-defined custom functions and statistics. nemo-inspector effectively expedited the cleaning process of the gsm-plus dataset, reducing the number of low-quality samples from 46.99% to 19.51%. furthermore, the tool helped detect generation issues in openmath models that led to a 1.92% drop in accuracy for these models on the math dataset and 4.17% on the gsm8k dataset for meta-llama-3-8b model fine-tuned on data generated from @xcite