Scaling Laws For Dense Retrieval

• URL: http://arxiv.org/abs/2403.18684v2
• Date: Mon, 15 Jul 2024 14:48:09 GMT
• Title: Scaling Laws For Dense Retrieval
• Authors: Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu,
• Abstract summary: We investigate whether the performance of dense retrieval models follows the scaling law as other neural models. Results indicate that, under our settings, the performance of dense retrieval models follows a precise power-law scaling related to the model size and the number of annotations.
• Score: 22.76001461620846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neural models frequently adheres to predictable scaling laws, correlated with factors such as training set size and model size. This insight is invaluable, especially as large-scale experiments grow increasingly resource-intensive. Yet, such scaling law has not been fully explored in dense retrieval due to the discrete nature of retrieval metrics and complex relationships between training data and model sizes in retrieval tasks. In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models. We propose to use contrastive log-likelihood as the evaluation metric and conduct extensive experiments with dense retrieval models implemented with different numbers of parameters and trained with different amounts of annotated data. Results indicate that, under our settings, the performance of dense retrieval models follows a precise power-law scaling related to the model size and the number of annotations. Additionally, we examine scaling with prevalent data augmentation methods to assess the impact of annotation quality, and apply the scaling law to find the best resource allocation strategy under a budget constraint. We believe that these insights will significantly contribute to understanding the scaling effect of dense retrieval models and offer meaningful guidance for future research endeavors.

Related papers

• Unified Neural Network Scaling Laws and Scale-time Equivalence [10.918504301310753]
  We present a novel theoretical characterization of how three factors -- model size, training time, and data volume -- interact to determine the performance of deep neural networks. We first establish a theoretical and empirical equivalence between scaling the size of a neural network and increasing its training time proportionally. We then combine scale-time equivalence with a linear model analysis of double descent to obtain a unified theoretical scaling law.
  arXiv  Detail & Related papers  (2024-09-09T16:45:26Z)
• Information-Theoretic Foundations for Neural Scaling Laws [20.617552198581024]
  We develop information-theoretic foundations for neural scaling laws. We observe that the optimal relation between data and model size is linear, up to logarithmic factors.
  arXiv  Detail & Related papers  (2024-06-28T02:20:54Z)
• Mixtures of Experts Unlock Parameter Scaling for Deep RL [54.26191237981469]
  In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules into value-based networks results in more parameter-scalable models. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.
  arXiv  Detail & Related papers  (2024-02-13T17:18:56Z)
• A Dynamical Model of Neural Scaling Laws [79.59705237659547]
  We analyze a random feature model trained with gradient descent as a solvable model of network training and generalization. Our theory shows how the gap between training and test loss can gradually build up over time due to repeated reuse of data.
  arXiv  Detail & Related papers  (2024-02-02T01:41:38Z)
• Scaling Laws Do Not Scale [54.72120385955072]
  Recent work has argued that as the size of a dataset increases, the performance of a model trained on that dataset will increase. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations.
  arXiv  Detail & Related papers  (2023-07-05T15:32:21Z)
• Inverse Scaling: When Bigger Isn't Better [80.42834197416444]
  Large language models (LMs) show predictable improvements to overall loss with increased scale. We present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale.
  arXiv  Detail & Related papers  (2023-06-15T20:11:23Z)
• A Solvable Model of Neural Scaling Laws [72.8349503901712]
  Large language models with a huge number of parameters, when trained on near internet-sized number of tokens, have been empirically shown to obey neural scaling laws. We propose a statistical model -- a joint generative data model and random feature model -- that captures this neural scaling phenomenology. Key findings are the manner in which the power laws that occur in the statistics of natural datasets are extended by nonlinear random feature maps.
  arXiv  Detail & Related papers  (2022-10-30T15:13:18Z)
• Revisiting Neural Scaling Laws in Language and Vision [43.57394336742374]
  We argue for a more rigorous methodology based on the extrapolation loss, instead of reporting the best-fitting parameters. We present a recipe for estimating scaling law parameters reliably from learning curves. We demonstrate that it extrapolates more accurately than previous methods in a wide range of architecture families across several domains.
  arXiv  Detail & Related papers  (2022-09-13T09:41:51Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments [42.793379799720434]
  We investigate whether scaling laws can be used to accelerate model development. We find that scaling laws emerge at finetuning time in some NLP tasks. For tasks where scaling laws exist, they can be used to predict the performance of larger models.
  arXiv  Detail & Related papers  (2022-02-13T19:13:00Z)

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.