Bridging the Data Provenance Gap Across Text, Speech and Video

• URL: http://arxiv.org/abs/2412.17847v2
• Date: Wed, 19 Feb 2025 03:05:56 GMT
• Title: Bridging the Data Provenance Gap Across Text, Speech and Video
• Authors: Shayne Longpre, Nikhil Singh, Manuel Cherep, Kushagra Tiwary, Joanna Materzynska, William Brannon, Robert Mahari, Naana Obeng-Marnu, Manan Dey, Mohammed Hamdy, Nayan Saxena, Ahmad Mustafa Anis, Emad A. Alghamdi, Vu Minh Chien, Da Yin, Kun Qian, Yizhi Li, Minnie Liang, An Dinh, Shrestha Mohanty, Deividas Mataciunas, Tobin South, Jianguo Zhang, Ariel N. Lee, Campbell S. Lund, Christopher Klamm, Damien Sileo, Diganta Misra, Enrico Shippole, Kevin Klyman, Lester JV Miranda, Niklas Muennighoff, Seonghyeon Ye, Seungone Kim, Vipul Gupta, Vivek Sharma, Xuhui Zhou, Caiming Xiong, Luis Villa, Stella Biderman, Alex Pentland, Sara Hooker, Jad Kabbara,
• Abstract summary: We conduct the largest and first-of-its-kind longitudinal audit across modalities of popular text, speech, and video datasets. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets.
• Score: 67.72097952282262
• License:
• Abstract: Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities--popular text, speech, and video datasets--from their detailed sourcing trends and use restrictions to their geographical and linguistic representation. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets, eclipsing all other sources since 2019. Secondly, tracing the chain of dataset derivations we find that while less than 33% of datasets are restrictively licensed, over 80% of the source content in widely-used text, speech, and video datasets, carry non-commercial restrictions. Finally, counter to the rising number of languages and geographies represented in public AI training datasets, our audit demonstrates measures of relative geographical and multilingual representation have failed to significantly improve their coverage since 2013. We believe the breadth of our audit enables us to empirically examine trends in data sourcing, restrictions, and Western-centricity at an ecosystem-level, and that visibility into these questions are essential to progress in responsible AI. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire multimodal audit, allowing practitioners to trace data provenance across text, speech, and video.

Related papers

• BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages [93.92804151830744]
  We present BRIGHTER, a collection of emotion-annotated datasets in 28 different languages. We describe the data collection and annotation processes and the challenges of building these datasets. We show that BRIGHTER datasets are a step towards bridging the gap in text-based emotion recognition.
  arXiv  Detail & Related papers  (2025-02-17T15:39:50Z)
• Cultural Fidelity in Large-Language Models: An Evaluation of Online Language Resources as a Driver of Model Performance in Value Representation [0.0]
  We show that the ability of GPT-4o to reflect societal values of a country correlates with the availability of digital resources in that language. Weaker performance in low-resource languages, especially prominent in the Global South, may worsen digital divides.
  arXiv  Detail & Related papers  (2024-10-14T13:33:00Z)
• Multilingual Diversity Improves Vision-Language Representations [66.41030381363244]
  Pre-training on this dataset outperforms using English-only or English-dominated datasets on ImageNet. On a geographically diverse task like GeoDE, we also observe improvements across all regions, with the biggest gain coming from Africa.
  arXiv  Detail & Related papers  (2024-05-27T08:08:51Z)
• A Study on Scaling Up Multilingual News Framing Analysis [23.80807884935475]
  This study explores the possibility of dataset creation through crowdsourcing. We first extend framing analysis beyond English news to a multilingual context. We also present a novel benchmark in Bengali and Portuguese on the immigration and same-sex marriage domains.
  arXiv  Detail & Related papers  (2024-04-01T21:02:18Z)
• The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI [41.32981860191232]
  Legal and machine learning experts to systematically audit and trace 1800+ text datasets. Our landscape analysis highlights the sharp divides in composition and focus of commercially open vs closed datasets. frequent miscategorization of licenses on widely used dataset hosting sites, with license omission of 70%+ and error rates of 50%+.
  arXiv  Detail & Related papers  (2023-10-25T17:20:26Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• No Language Left Behind: Scaling Human-Centered Machine Translation [69.28110770760506]
  We create datasets and models aimed at narrowing the performance gap between low and high-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-11T07:33:36Z)
• Content4All Open Research Sign Language Translation Datasets [27.36513138911057]
  We release six datasets comprised of 190 hours of footage on the larger domain of news. From this, 20 hours of footage have been annotated by Deaf experts and interpreters and is made publicly available for research purposes.
  arXiv  Detail & Related papers  (2021-05-05T22:14:53Z)
• Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets [21.375943264243144]
  We manually audit the quality of 205 language-specific corpora released with five major public datasets. We find that at least 15 corpora are completely erroneous, and a significant fraction contains less than 50% sentences of acceptable quality. We demonstrate that these issues are easy to detect even for non-speakers of the languages in question, and supplement the human judgements with automatic analyses.
  arXiv  Detail & Related papers  (2021-03-22T17:30:33Z)
• Facebook AI's WMT20 News Translation Task Submission [69.92594751788403]
  This paper describes Facebook AI's submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil -> English and Inuktitut -> English. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain.
  arXiv  Detail & Related papers  (2020-11-16T21:49:00Z)

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.