Fix your Models by Fixing your Datasets

• URL: http://arxiv.org/abs/2112.07844v1
• Date: Wed, 15 Dec 2021 02:41:50 GMT
• Title: Fix your Models by Fixing your Datasets
• Authors: Atindriyo Sanyal, Vikram Chatterji, Nidhi Vyas, Ben Epstein, Nikita Demir, Anthony Corletti
• Abstract summary: Current machine learning tools lack streamlined processes for improving the data quality. We introduce a systematic framework for finding noisy or mislabelled samples in the dataset. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies.
• Score: 0.6058427379240697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The quality of underlying training data is very crucial for building performant machine learning models with wider generalizabilty. However, current machine learning (ML) tools lack streamlined processes for improving the data quality. So, getting data quality insights and iteratively pruning the errors to obtain a dataset which is most representative of downstream use cases is still an ad-hoc manual process. Our work addresses this data tooling gap, required to build improved ML workflows purely through data-centric techniques. More specifically, we introduce a systematic framework for (1) finding noisy or mislabelled samples in the dataset and, (2) identifying the most informative samples, which when included in training would provide maximal model performance lift. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies, and are confident this work will form the basis for ML teams to perform more intelligent data discovery and pruning.

Related papers

• Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [71.2981957820888]
  We propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets. The framework initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method. The generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality.
  arXiv  Detail & Related papers  (2024-11-21T02:30:53Z)
• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs [11.24476329991465]
  Training large language models (LLMs) for external tool usage is a rapidly expanding field. The absence of systematic data quality checks poses complications for properly training and testing models. We propose two approaches for assessing the reliability of data for training LLMs to use external tools.
  arXiv  Detail & Related papers  (2024-09-24T17:20:02Z)
• Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond [38.89457061559469]
  We propose an innovative methodology that automates dataset creation with negligible cost and high efficiency. We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data. We design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning.
  arXiv  Detail & Related papers  (2024-08-21T04:45:12Z)
• An Integrated Data Processing Framework for Pretraining Foundation Models [57.47845148721817]
  Researchers and practitioners often have to manually curate datasets from difference sources. We propose a data processing framework that integrates a Processing Module and an Analyzing Module. The proposed framework is easy to use and highly flexible.
  arXiv  Detail & Related papers  (2024-02-26T07:22:51Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models [56.27786433792638]
  STAR is a data generation method that leverages Large Language Models (LLMs) to synthesize data instances. We design fine-grained step-by-step instructions to obtain the initial data instances. Our experiments show that the data generated by STAR significantly improve the performance of low-resource event extraction and relation extraction tasks.
  arXiv  Detail & Related papers  (2023-05-24T12:15:19Z)
• Optimizing the AI Development Process by Providing the Best Support Environment [0.756282840161499]
  Main stages of machine learning are problem understanding, data management, model building, model deployment and maintenance. The framework was built using python language to perform data augmentation using deep learning advancements.
  arXiv  Detail & Related papers  (2023-04-29T00:44:50Z)
• Evaluating and Crafting Datasets Effective for Deep Learning With Data Maps [0.0]
  Training on large datasets often requires excessive system resources and an infeasible amount of time. For supervised learning, large datasets require more time for manually labeling samples. We propose a method of curating smaller datasets with comparable out-of-distribution model accuracy after an initial training session.
  arXiv  Detail & Related papers  (2022-08-22T03:30:18Z)
• Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation [0.5161531917413706]
  We develop Data Augmentation from Proficient Pre-Training of Robust Generative Adrial Networks (DAPPER GAN) DAPPER GAN is an ML analytics support tool that automatically generates novel views of training images. We experimentally evaluate this technique on the Stanford Cars dataset, demonstrating improved vehicle make and model classification accuracy.
  arXiv  Detail & Related papers  (2020-08-12T15:29:11Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.