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 James Validad 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
Primary area: datasets and benchmarks
Keywords: training data, audit, speech, video, text

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.

Decision: accept (poster)
Reviewer scores: 5, 5, 8, 8

Reviews and discussion:
<thread>
creation date:
1730279233418

thread_type:
official_review

<thread_object>
creator: reviewer_bawp

<content>
summary:
The authors conduct a comprehensive audit across modalities—popular text, speech, and video datasets—from their detailed sourcing trends and use restrictions to their geographical and linguistic representation.

strengths:
1. This is an extremely exciting work for multi-modal researchers, as one of the main bottlenecks is how to get balanced and good quality data from the wild. This work deepens the communities understanding of multi-modal data conditions at present and in the history.
2. The authors conducted rigorous data collection and analysis work, which deserves much credit.
3. The insights provided are valuable and interesting, which may provide great inspirations for people curating multi-modal data from real world.

weaknesses:
1. I feel the data collection specifications are limited. Though the authors has mentioned that the discussion for geographical and linguistic representation is limited, I think more discussion should be casted on the data collection methods, as this is the foundation of all discussions in this work. How to ensure the recall and precision of data collection? The data and its conclusion could easily get biased when missing or mistakenly putting a large dataset into the data pool.

questions:
1. I'm curious that did you perform analysis on the portion of synthetic data and their characteristics? I think this is one of the most concerned topics by far, though it's really hard to correctly collect all the data.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730531110757

thread_type:
official_review

<thread_object>
creator: reviewer_aq3r

<content>
summary:
This work conducts a large-scale and comprehensive audit of nearly 4,000 text, speech, and video datasets between 1990-2024, covering a wide range of tasks, languages, sources, organizations, and countries. Specifically, the authors analyze data trends for the state of data permissions (licenses and terms), sourcing (the web, human annotation, and synthetic generation), and representation (of tasks, organizations, languages, and countries). The datasets included in the audit include those that are publicly available, widely used for general-purpose model development, and relevant to generative tasks. Overall, the authors analyzed 3.7k text, 95 speech, and 104 video datasets. 

The analysis revealed an increasing use of data from web-crawled, social media, and synthetic sources, driven by scaling laws. This was particularly prevalent in speech and video datasets, with YouTube being the most prominent source. The contribution of synthetic text data has also rapidly become significant in recent years. Additionally, the authors found inconsistencies in data source terms and documented license restrictions, with the former being much more restrictive. Further, many data collections do not clarify commercially licensed versus restrictive datasets. Finally, the analysis revealed that while there has been an increase in the languages and countries represented in the datasets, the relative contribution remains Western-centric.

strengths:
* The paper provides novel insights from a large-scale and comprehensive audit of widely used datasets across text, speech, and video modalities.
* The paper is well-written and organized.
* Larger implications of the authors’ findings are discussed, highlighting challenges and suggestions for practitioners.

weaknesses:
* The audit does not include image datasets, which are widely used in multimodal settings.
* The analysis does not include the tasks that the datasets are designed for.
* Some more insights and analysis can be provided for the cause of observed trends- e.g. the sharp rise in encyclopedia-based and internet video-based sources in text and speech datasets after 2018, the drop in the Gini coefficient for geographic representation in video datasets after 2019, etc.

questions:
* Can the source of synthetic datasets (e.g. the models they were generated from) be determined? Are there any challenges in determining such sources?
* Why was the scope of the datasets limited to generative tasks?
* Was there a variation in the tasks represented by the data across the years? It would be interesting to observe emerging trends in AI research over the years (e.g. code generation and other domain-specific tasks).
* Was the correlation between data sources and language/geographical diversity analyzed?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730691542628

thread_type:
official_review

<thread_object>
creator: reviewer_kmty

<content>
summary:
In this paper, the authors present an audit of nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. They further present their analysis of data sources, geographical and multilingual inclusion.

strengths:
The paper is well written and easy to understand. The authors explore an important topic relevant to extremely fast-paced growth of AI models and their adoption. They explore a large-scale collection of datasets across three modalities: text, speech and video. They further present interesting analysis highlighting the community inclusion for dataset creation and the restriction on the usage of the datasets. The study brings out the inequality in geographical representation and the multilingual representation and furthermore the situation has not improved significantly over the last decade on most measures.

weaknesses:
I find the lack of call-to-action from the authors. There are no concreate directions presented in the paper that could help improve the situation. A mere analysis of the landscape is probably not going to be of much help given the pace with which the field is evolving. They have surveyed so many papers which should have nudged them to form an opinion on the next course of action for the community. 

Also, since this field is growing rapidly, the analysis presented here would become obsolete very quickly. What might help in the long-term is to build a tool that can automate the analysis presented here with such dedicated effort.

questions:
See my comments in the weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1730699922599

thread_type:
official_review

<thread_object>
creator: reviewer_yvyf

<content>
summary:
The paper conducts a large-scale dataset audit across text, speech, and video modalities, covering near 4000 public datasets between 1990-2024 by investigating their sources, use restrictions, and their geographical & linguistic representation. It is found that 1) Many multimodal ML datasets are from web-crawled and social media platforms; 2) Inconsistencies between dataset licenses and their source's restrictions prevail; and 3) Inequality in geographical representation remains very high, and geographical & linguistic representation has not significantly improved for many years.

strengths:
• The paper is well-motivated: Most previous works has primarily focused on text datasets, or a single feature or dataset. The paper conducted a multimodal and multi-feature dataset, which addresses this gap.

• The scale of this study is impressive: It covers nearly 4000 datasets, 3 modalities (text, speech, video) and a time span of over 30 years (1990~2024), providing a comprehensive view to dataset provenance study.

• The study points out vulnerabilities of current dataset sources. For example, the paper discovers prevailing inconsistencies between dataset licenses and their source's restrictions, alarming the research community about potential legal breaches. The paper also reveals that many measures to address geographical & linguistic fairness have failed, surprisingly, which motivates the research community to retrospect these measures and potentially propose new (effective) ones.

weaknesses:
• The paper mentions a rise in synthetic data, but does not give any in-depth analysis of synthetic data v.s. non-synthetic data. I think it is worthwhile to analyze the source distribution of synthetic data v.s. non-synthetic data, so that researchers can know the search space where they are more likely to acquire desired data when they need human-written or machine-generated data.

• The paper is not technically savvy, so it is crucial for the paper to highlight the importance of its insights. Given the large-scale of this study (nearly 4000 datasets) and the engagement of domain experts for annotation tasks, the study should be costly. But the paper did not make it very clear that why studying data provenance (probably with such high cost) is valuable at the first place. Why not use the same annotation labors to create new dataset resources instead, but studying the provenance of existing dataset (intuitively, the former one can be more valuable)?

questions:
• Given that the study includes datasets from 67 countries, when studying data licenses or terms of use, are difference in legal regulations of copyrights between countries/regions taken into consideration?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732215046275

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
We would like to thank the reviewers for their positive and constructive feedback! While we respond to each reviewer individually below, multiple asked about the synthetic data trends in our work. We agree this is an important topic, and would add useful insights for practitioners. Here we have consolidated some of the statistics uncovered by our audit:

* The top models used in generating datasets are mainly from OpenAI, with some open-weights models also popular. The top 5 consists of ChatGPT, version unspecified (15.0% of synthetic datasets), GPT-4 (14.4%), BART (10.1%), GPT-3 (8.3%) and GPT-3.5-Turbo (4.9%). 15.2% of datasets also used some kind of dataset-specific non-neural templating scheme.
* The average synthetic dataset also has notably longer turns (in tokens) than the average natural dataset: 1,756 tokens vs 1,065, a 64.8% increase.
* Synthetic dataset’s task distribution is shifted towards longer form, open-generation and creative tasks. For example, 88.1% of natural datasets contain classification tasks, vs only 66.3% of synthetic datasets. Natural data is also more likely to cover translation than synthetic data (72.4% of datasets vs only 22.9% of synthetic datasets). Both natural and synthetic data, however, had similar rates of creativity and Q&A tasks.
* Both natural and synthetic datasets are mostly English-only, though synthetic datasets are slightly more likely to be English-only than natural datasets (76.3% vs 69.6%).

All of these annotations, along with the analysis tools used to generate these findings will be open sourced. If the reviewers agree, we will add a subsection of the paper, dedicated to these empirical findings on the rise of synthetic data.
</content>
</thread_object>

</thread>
<thread>
creation date:
1739413177497

thread_type:
public_comment

<thread_object>
creator: ~jonathan_bennion1

<content>
title:
Fallacy in the premise of this paper not addressed

comment:
The premise of this paper focuses on data provenance, linguistic representation, and dataset transparency, without discussing the structural challenges of tokenization from a technical point of view.

Regarding the logical fallacy, calling an issue an "injustice" when it is fixable could be an example of a false dilemma, since assuming the problem is inherently unfair (rather than a technical or policy issue that could be addressed).
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper conducts a large-scale audit of nearly 4,000 public datasets (text, speech, and video) spanning the years 1990–2024 covering 608 languages, 798 data sources, 659 organizations, and 67 countries. It analyzes inconsistencies between dataset licenses and the terms of their data sources, noting that source terms are often more restrictive. It also reports that despite some increase in the number of languages and countries represented, the relative contribution remains Western-centric. 

**Strengths identified**:
1. The paper is comprehensive in coverage.

2. It addresses a timely and critical topic relevant to the fast-paced growth of AI models and their reliance on datasets.

3. It provides novel insights into dataset sourcing, use restrictions, and geographical and linguistic representation.

4. The findings are valuable for advancing dataset governance and inclusivity and for understanding current and historical multimodal data conditions.

**Weakness that if addressed can strengthen the paper further**:
1. Limited depth in analyzing synthetic data, dataset tasks, and observed trends.

2. Lack of actionable outcomes or tools to address the issues raised: The paper lacks a clear call-to-action or concrete recommendations for improving the issues highlighted in the audit. It misses an opportunity to provide tools or frameworks that could automate similar audits in the future, making the findings more enduring. 

3. Weak justification for the study's value compared to alternative efforts, like creating new datasets.