IR2: Information Regularization for Information Retrieval
- URL: http://arxiv.org/abs/2402.16200v1
- Date: Sun, 25 Feb 2024 21:25:06 GMT
- Title: IR2: Information Regularization for Information Retrieval
- Authors: Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Weili Cao, Ramamohan
Paturi, Leon Bergen
- Abstract summary: This paper introduces IR2, Information Regularization for Information Retrieval, a technique for reducing overfitting during synthetic data generation.
Experimental results indicate that our regularization techniques not only outperform previous synthetic query generation methods on the tasks considered but also reduce cost by up to 50%.
- Score: 2.4816250611120547
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Effective information retrieval (IR) in settings with limited training data,
particularly for complex queries, remains a challenging task. This paper
introduces IR2, Information Regularization for Information Retrieval, a
technique for reducing overfitting during synthetic data generation. This
approach, representing a novel application of regularization techniques in
synthetic data creation for IR, is tested on three recent IR tasks
characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook.
Experimental results indicate that our regularization techniques not only
outperform previous synthetic query generation methods on the tasks considered
but also reduce cost by up to 50%. Furthermore, this paper categorizes and
explores three regularization methods at different stages of the query
synthesis pipeline-input, prompt, and output-each offering varying degrees of
performance improvement compared to models where no regularization is applied.
This provides a systematic approach for optimizing synthetic data generation in
data-limited, complex-query IR scenarios. All code, prompts and synthetic data
are available at
https://github.com/Info-Regularization/Information-Regularization.
Related papers
- Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A
Comprehensive Benchmark [56.8042116967334]
Synthetic data serves as an alternative in training machine learning models.
ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task.
This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
arXiv Detail & Related papers (2023-10-25T20:32:02Z) - Zero-shot Composed Text-Image Retrieval [72.43790281036584]
We consider the problem of composed image retrieval (CIR)
It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
arXiv Detail & Related papers (2023-06-12T17:56:01Z) - Bridging the Gap: Enhancing the Utility of Synthetic Data via
Post-Processing Techniques [7.967995669387532]
generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data.
We propose three novel post-processing techniques to improve the quality and diversity of the synthetic dataset.
Experiments show that Gap Filler (GaFi) effectively reduces the gap with real-accuracy scores to an error of 2.03%, 1.78%, and 3.99% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, respectively.
arXiv Detail & Related papers (2023-05-17T10:50:38Z) - Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
We show how the generative process affects the downstream ML task.
We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
arXiv Detail & Related papers (2023-05-16T07:30:29Z) - A New Benchmark: On the Utility of Synthetic Data with Blender for Bare
Supervised Learning and Downstream Domain Adaptation [42.2398858786125]
Deep learning in computer vision has achieved great success with the price of large-scale labeled training data.
The uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist.
To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization.
arXiv Detail & Related papers (2023-03-16T09:03:52Z) - FairGen: Fair Synthetic Data Generation [0.3149883354098941]
We propose a pipeline to generate fairer synthetic data independent of the GAN architecture.
We claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples.
arXiv Detail & Related papers (2022-10-24T08:13:47Z) - Incorporating Relevance Feedback for Information-Seeking Retrieval using
Few-Shot Document Re-Ranking [56.80065604034095]
We introduce a kNN approach that re-ranks documents based on their similarity with the query and the documents the user considers relevant.
To evaluate our different integration strategies, we transform four existing information retrieval datasets into the relevance feedback scenario.
arXiv Detail & Related papers (2022-10-19T16:19:37Z) - Towards Lifelong Learning of End-to-end ASR [81.15661413476221]
Lifelong learning aims to enable a machine to sequentially learn new tasks from new datasets describing the changing real world without forgetting the previously learned knowledge.
An overall relative reduction of 28.7% in WER was achieved compared to the fine-tuning baseline when sequentially learning on three very different benchmark corpora.
arXiv Detail & Related papers (2021-04-04T13:48:53Z) - A study of traits that affect learnability in GANs [0.0]
Generative Adversarial Networks GANs are algorithmic architectures that use two neural networks, pitting one against the opposite so as to come up with new, synthetic instances of data that can pass for real data.
In this paper, we perform empirical experiments using parameterized synthetic datasets to probe what traits affect learnability.
arXiv Detail & Related papers (2020-11-27T13:31:37Z) - Partially Conditioned Generative Adversarial Networks [75.08725392017698]
Generative Adversarial Networks (GANs) let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset.
With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.
In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy.
arXiv Detail & Related papers (2020-07-06T15:59:28Z)
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.