Can Foundation Models Wrangle Your Data?

• URL: http://arxiv.org/abs/2205.09911v1
• Date: Fri, 20 May 2022 00:53:43 GMT
• Title: Can Foundation Models Wrangle Your Data?
• Authors: Avanika Narayan, Ines Chami, Laurel Orr, Christopher R\'e
• Abstract summary: Foundation Models (FMs) are models trained on large corpora of data that can generalize to new tasks without task-specific finetuning. This paper aims to understand an underexplored area of FMs: classical data tasks like cleaning and integration. We find that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks.
• Score: 13.11923018654058
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims to understand an underexplored area of FMs: classical data tasks like cleaning and integration. As a proof-of-concept, we cast three data cleaning and integration tasks as prompting tasks and evaluate the performance of FMs on these tasks. We find that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks. We identify specific research challenges and opportunities that these models present, including challenges with private and temporal data, and opportunities to make data driven systems more accessible to non-experts. We make our code and experiments publicly available at: https://github.com/HazyResearch/fm_data_tasks.

Related papers

• Specialized Foundation Models Struggle to Beat Supervised Baselines [60.23386520331143]
  We look at three modalities -- genomics, satellite imaging, and time series -- with multiple recent FMs and compare them to a standard supervised learning workflow. We find that it is consistently possible to train simple supervised models that match or even outperform the latest foundation models.
  arXiv  Detail & Related papers  (2024-11-05T04:10:59Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution [16.272314073324626]
  Foundation models (FMs) are a popular topic of research in AI. In this work, we compare the performance of FMs to finetuned pre-trained supervised models in the task of semantic segmentation. We see that finetuned models consistently outperform the FMs tested, even in cases were data is scarce.
  arXiv  Detail & Related papers  (2024-09-05T17:59:32Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• Unified machine learning tasks and datasets for enhancing renewable energy [0.8356833388425764]
  We introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets related to enhancing renewable energy. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model.
  arXiv  Detail & Related papers  (2023-11-12T15:30:44Z)
• Learn From Model Beyond Fine-Tuning: A Survey [78.80920533793595]
  Learn From Model (LFM) focuses on the research, modification, and design of foundation models (FM) based on the model interface. The study of LFM techniques can be broadly categorized into five major areas: model tuning, model distillation, model reuse, meta learning and model editing. This paper gives a comprehensive review of the current methods based on FM from the perspective of LFM.
  arXiv  Detail & Related papers  (2023-10-12T10:20:36Z)
• CHORUS: Foundation Models for Unified Data Discovery and Exploration [6.85448651843431]
  We show that foundation models are highly applicable to the data discovery and data exploration domain. We show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. This suggests a future direction in which disparate data management tasks can be unified under foundation models.
  arXiv  Detail & Related papers  (2023-06-16T03:58:42Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)
• Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning [1.0062040918634414]
  Few-shot learning algorithms are designed to generalize well to new tasks with limited data. We introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data.
  arXiv  Detail & Related papers  (2020-09-23T17:01:09Z)

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.