Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources

• URL: http://arxiv.org/abs/2307.02460v1
• Date: Wed, 5 Jul 2023 17:33:41 GMT
• Title: Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
• Authors: Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia
• Abstract summary: This paper proposes a framework called or>, which predicts model performance and supports data selection decisions based on partial samples of prospective data sources. or> significantly improves existing performance scaling approaches in terms of both accuracy of performance inference and computation costs associated with constructing the performance. Also, or> outperforms by a wide margin in data selection effectiveness compared to a range of other off-the-shelf solutions.
• Score: 9.359395812292291
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditionally, data selection has been studied in settings where all samples from prospective sources are fully revealed to a machine learning developer. However, in practical data exchange scenarios, data providers often reveal only a limited subset of samples before an acquisition decision is made. Recently, there have been efforts to fit scaling laws that predict model performance at any size and data source composition using the limited available samples. However, these scaling functions are black-box, computationally expensive to fit, highly susceptible to overfitting, or/and difficult to optimize for data selection. This paper proposes a framework called <projektor>, which predicts model performance and supports data selection decisions based on partial samples of prospective data sources. Our approach distinguishes itself from existing work by introducing a novel *two-stage* performance inference process. In the first stage, we leverage the Optimal Transport distance to predict the model's performance for any data mixture ratio within the range of disclosed data sizes. In the second stage, we extrapolate the performance to larger undisclosed data sizes based on a novel parameter-free mapping technique inspired by neural scaling laws. We further derive an efficient gradient-based method to select data sources based on the projected model performance. Evaluation over a diverse range of applications demonstrates that <projektor> significantly improves existing performance scaling approaches in terms of both the accuracy of performance inference and the computation costs associated with constructing the performance predictor. Also, <projektor> outperforms by a wide margin in data selection effectiveness compared to a range of other off-the-shelf solutions.

Related papers

• Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs [18.242110417706]
  This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. We show the optimality of this approach for fine-tuning tasks under certain conditions. Our proposed method is significantly faster than existing techniques, scaling to millions of samples within a single GPU hour.
  arXiv  Detail & Related papers  (2024-05-05T00:08:00Z)
• 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)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• DsDm: Model-Aware Dataset Selection with Datamodels [81.01744199870043]
  Standard practice is to filter for examples that match human notions of data quality. We find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. Our framework avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks.
  arXiv  Detail & Related papers  (2024-01-23T17:22:00Z)
• Towards Free Data Selection with General-Purpose Models [71.92151210413374]
  A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. Current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods.
  arXiv  Detail & Related papers  (2023-09-29T15:50:14Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning [13.804184845195296]
  We propose a preprocessing model that learns to optimise input data for a specific target vision model. We investigate several out-of-distribution scenarios in the context of semantic segmentation for autonomous vehicles. We demonstrate that our approach can enable performance on such data comparable to that of a finetuned model.
  arXiv  Detail & Related papers  (2022-11-29T14:06:35Z)
• Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes [47.880781811936345]
  We propose a novel framework for fine-tuning pretrained language models (LM) Our prototypical fine-tuning approach can automatically adjust the model capacity according to the number of data points and the model's inherent attributes.
  arXiv  Detail & Related papers  (2022-11-24T14:38:08Z)
• Differentiable Neural Input Search for Recommender Systems [26.88124270897381]
  Differentiable Neural Input Search (DNIS) is a method that searches for mixed feature embedding dimensions in a more flexible space. DNIS is model-agnostic and can be seamlessly incorporated with existing latent factor models for recommendation.
  arXiv  Detail & Related papers  (2020-06-08T10:43:59Z)

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.