Reframing Data Value for Large Language Models Through the Lens of Plausibility

• URL: http://arxiv.org/abs/2409.00284v2
• Date: Tue, 15 Oct 2024 20:04:22 GMT
• Title: Reframing Data Value for Large Language Models Through the Lens of Plausibility
• Authors: Mohamad Rida Rammal, Ruida Zhou, Suhas Diggavi,
• Abstract summary: We propose an alternative perspective on the data value problem for language models. We develop a novel value function that is computationally tractable and derived from first principles with provable properties.
• Score: 6.697702130929693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data valuation seeks to answer the important question, "How much is this data worth?" Existing data valuation methods have largely focused on discriminative models, primarily examining data value through the lens of its utility in training. However, with the push for ever-larger language models, relying on valuation methods that require training becomes increasingly expensive and dependent on specific techniques. We propose an alternative perspective on the data value problem for language models, centering around the plausibility of the data. We posit that data holds lesser value if it can be plausibly generated by the model itself. Starting from some intuitive criteria that align with our notions of valuable data, we develop a novel value function that is computationally tractable and derived from first principles with provable properties. We conduct a theoretical analysis of our value function and evaluate it across multiple scenarios and datasets.

Related papers

• How to Select Datapoints for Efficient Human Evaluation of NLG Models? [57.60407340254572]
  We develop a suite of selectors to get the most informative datapoints for human evaluation. We show that selectors based on variance in automated metric scores, diversity in model outputs, or Item Response Theory outperform random selection. In particular, we introduce source-based estimators, which predict item usefulness for human evaluation just based on the source texts.
  arXiv  Detail & Related papers  (2025-01-30T10:33:26Z)
• An Instrumental Value for Data Production and its Application to Data Pricing [107.98697414652479]
  This paper develops an approach for capturing the instrumental value of data production processes. We show how they connect to classic notions of information design and signals in information economics.
  arXiv  Detail & Related papers  (2024-12-24T03:53:57Z)
• Is Data Valuation Learnable and Interpretable? [3.9325957466009203]
  Current data valuation methods ignore the interpretability of the output values. This study aims to answer an important question: is data valuation learnable and interpretable?
  arXiv  Detail & Related papers  (2024-06-03T08:13:47Z)
• Scaling Laws for the Value of Individual Data Points in Machine Learning [55.596413470429475]
  We introduce a new perspective by investigating scaling behavior for the value of individual data points. We provide learning theory to support our scaling law, and we observe empirically that it holds across diverse model classes. Our work represents a first step towards understanding and utilizing scaling properties for the value of individual data points.
  arXiv  Detail & Related papers  (2024-05-30T20:10:24Z)
• Neural Dynamic Data Valuation [4.286118155737111]
  We propose a novel data valuation method from the perspective of optimal control, named the neural dynamic data valuation (NDDV) Our method has solid theoretical interpretations to accurately identify the data valuation via the sensitivity of the data optimal control state. In addition, we implement a data re-weighting strategy to capture the unique features of data points, ensuring fairness through the interaction between data points and the mean-field states.
  arXiv  Detail & Related papers  (2024-04-30T13:39:26Z)
• EcoVal: An Efficient Data Valuation Framework for Machine Learning [11.685518953430554]
  Existing Shapley value based frameworks for data valuation in machine learning are computationally expensive. We introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner.
  arXiv  Detail & Related papers  (2024-02-14T16:21:47Z)
• JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction [59.94977231327573]
  We propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE. Two variants of our model are designed for open-world and closed-world scenarios. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines.
  arXiv  Detail & Related papers  (2023-11-07T18:36:16Z)
• LAVA: Data Valuation without Pre-Specified Learning Algorithms [20.578106028270607]
  We introduce a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions.
  arXiv  Detail & Related papers  (2023-04-28T19:05:16Z)
• GMValuator: Similarity-based Data Valuation for Generative Models [41.76259565672285]
  We introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to provide data valuation for generation tasks. GMValuator is extensively evaluated on various datasets and generative architectures to demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2023-04-21T02:02:02Z)
• Learning to be a Statistician: Learned Estimator for Number of Distinct Values [54.629042119819744]
  Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems. In this work, we focus on how to derive accurate NDV estimations from random (online/offline) samples. We propose to formulate the NDV estimation task in a supervised learning framework, and aim to learn a model as the estimator.
  arXiv  Detail & Related papers  (2022-02-06T15:42:04Z)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.