Data quality or data evaluation is sometimes a task as important as
collecting a large volume of data when it comes to generating accurate
artificial intelligence models. In fact, being able to evaluate the data can
lead to a larger database that is better suited to a particular problem because
we have the ability to filter out data obtained automatically of dubious
quality. In this paper we present RLBoost, an algorithm that uses deep
reinforcement learning strategies to evaluate a particular dataset and obtain a
model capable of estimating the quality of any new data in order to improve the
final predictive quality of a supervised learning model. This solution has the
advantage that of being agnostic regarding the supervised model used and,
through multi-attention strategies, takes into account the data in its context
and not only individually. The results of the article show that this model
obtains better and more stable results than other state-of-the-art algorithms
such as LOO, DataShapley or DVRL.

本文介绍了 RLBoost 算法，它使用深度强化学习策略评估数据集并获得一个能够估计任何新数据质量的模型，以提高监督学习模型的最终预测质量。该算法通过多关注策略，考虑了数据的上下文信息，与其他现有算法相比具有更好和更稳定的结果。

RLBoost: 使用深度强化学习提升监督学习模型

RLBoost: Boosting Supervised Models using Deep Reinforcement Learning

With the success of large-scale pre-training and multilingual modeling in
Natural Language Processing (NLP), recent years have seen a proliferation of
large, web-mined text datasets covering hundreds of languages. We manually
audit the quality of 205 language-specific corpora released with five major
public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource
corpora have systematic issues: At least 15 corpora have no usable text, and a
significant fraction contains less than 50% sentences of acceptable quality. In
addition, many are mislabeled or use nonstandard/ambiguous language codes. We
demonstrate that these issues are easy to detect even for non-proficient
speakers, and supplement the human audit with automatic analyses. Finally, we
recommend techniques to evaluate and improve multilingual corpora and discuss
potential risks that come with low-quality data releases.

本文主要旨在探究如何评估及提高多语料库的质量，并针对少资源语言建议相关技术，同时也讨论低质量数据发布所可能带来的潜在风险。

一瞥即知质量：对多语言网络爬虫数据集的审计

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility.

利用模拟，本研究论证了使用已暴露于推荐算法的用户数据对建立或评估推荐系统会构成反馈循环，同化用户行为但不提高实用价值。