Image: ComplexDiscovery
[EDRM Editor’s Note: This article was first published here on April 26, 2023 and EDRM is grateful to Rob Robinson, editor and managing director of ComplexDiscovery, for permission to republish.]
[ComplexDiscovery Editor’s Background Note: The impact of organizations and entities on the output from Large Language Models (LLMs) can be more significant than one might initially anticipate. In some instances, specific resources within an industry can considerably influence how LLMs process and respond to information. One example of this influence can be observed by examining the Google C4 Dataset and searching for a non-comprehensive selection of domains from 55 eDiscovery-centric websites. While this exploration only offers a snapshot of selected resources from a non-all-inclusive list, it may provide valuable context for those evaluating the resource impact on LLMs and also highlight some tools that can help better understand the content populating LLMs. This deeper understanding can, in turn, contribute to shedding light on how selected eDiscovery resources may play a substantial role in shaping the knowledge and responses generated by LLMs – a role much more significant (or less important) than one might think.]
Industry Backgrounder
Exploring the Inclusion of eDiscovery-Centric Resources in the Google C4 Dataset: A Highly Selective Search
ComplexDiscovery*
Large language models, such as those developed by Google and OpenAI, are becoming increasingly sophisticated and pervasive in various industries. One such application of these models is in the eDiscovery ecosystem, which contains touchpoints ranging from cybersecurity and information governance to legal discovery. This article explores at a very high level the inclusion of selected eDiscovery-centric resources in the Google C4 Dataset. It also discusses why understanding this exploration may benefit professionals working in the eDiscovery ecosystem.
Google’s C4 Dataset and its Relevance to eDiscovery
Understanding the Google G4 Dataset
Google’s C4 (Colossal Clean Crawled Corpus) project aims to create a comprehensive and diverse dataset for training large language models. The dataset is built from web pages crawled by the CommonCrawl project and includes a diverse range of content in multiple languages. Google’s C4 Dataset serves as an essential foundation for developing more accurate and sophisticated language models that can understand and generate human-like text.
The C4 dataset from Google contains approximately 750GB of cleaned text data derived from CommonCrawl web pages. This large-scale dataset is utilized for training and improving large language models, such as those based on the GPT architecture.
CommonCrawl is an open-source initiative that crawls and archives publicly available web content. This vast repository of web-crawled data is invaluable for training large language models, as it provides a diverse and extensive source of text in multiple languages. The Common Crawl project significantly contributes to the C4 Dataset, enhancing its quality and usefulness for AI research.
The Role of large language models in eDiscovery
Large language models can potentially revolutionize the eDiscovery process by automating tasks ranging from document review to review reporting. These models can analyze vast amounts of data quickly and efficiently, identify relevant information, and generate insightful summaries or responses. As a result, they can save time, reduce costs, and improve the accuracy of eDiscovery outcomes.
Inclusion of eDiscovery-centric resources in the C4 Dataset
The presence of eDiscovery resources in the C4 Dataset is crucial for ensuring the accuracy and relevance of large language model outputs in the eDiscovery context. By training on high-quality eDiscovery resources, the models can better understand the domain-specific language, concepts, and best practices, leading to more reliable and valuable results for eDiscovery professionals.
ComplexDiscovery’s Non-Comprehensive List of eDiscovery Resources and Its Significance
Introduction to ComplexDiscovery’s resource listing
On March 9, 2023, ComplexDiscovery published a non-comprehensive list of potentially helpful eDiscovery-centric resources. These resources, ranging from analyst and research firms to industry associations and blogs, were designed to serve as a simple starting point for individuals seeking information related to eDiscovery.
Selection of resources from ComplexDiscovery’s list for analysis
Given the manageable size of this resource listing and the direct or indirect relevance to the eDiscovery ecosystem of each listed resource, ComplexDiscovery created a truncated listing from an initial grouping of 100+ resources and used the top-level domain names of those resources to search the C4 Dataset. This truncation, which included the removal of top-level domain duplicates for multiple resources on the same domain and removing resources not available at the time of the Google C4 Dataset snapshot, resulted in a list of 55 resource domains.
Top-level domain names search against the C4 Dataset
The objective of searching the top-level domain names of the selected resources within the C4 Dataset was to explore how a very targeted snapshot of eDiscovery resources might be represented in the C4 Dataset. This information on the representation of selected resources may help gauge how these resources are being used to train Google’s large language models in responding to inquiries and prompts related to eDiscovery.
The results of top-level domain name searches of 55 eDiscovery-centric resources are provided in the following table, as extracted from the C4 Dataset search capability resource featured in the Washington Post article titled “Inside the Secret List of Websites That Make AI Like ChatGPT Sound Smart.” The data is reported based on database rank, tokens, and the percentage of all tokens. The aggregated results for the selected resources below showcase the prevalence of content from these resources in the C4 Dataset.
Table: Selected eDiscovery Resources and the C4 Dataset. To search the dataset, click here.
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Showing 1 to 55 of 55 entries
Source: ComplexDiscovery and the Washington Post
Implications of eDiscovery Resource Representation in the C4 Dataset
Identifying potential biases and limitations
By analyzing the representation of eDiscovery resources in the C4 Dataset, professionals in the eDiscovery ecosystem can identify potential biases and limitations in the data used to train large language models. This knowledge may enable them to make more informed decisions about the reliability and applicability of AI-generated outputs in their work.
Enhancing the quality and diversity of data used to train large language models
Understanding the inclusion of eDiscovery resources in the C4 Dataset can also help researchers and developers improve the quality and diversity of data used to train large language models. By incorporating a more comprehensive range of eDiscovery-centric resources, models may become better equipped to generate more accurate and relevant responses in the eDiscovery context.
Addressing the needs of cybersecurity, information governance, and legal discovery professionals
By exploring the eDiscovery resources represented in the C4 Dataset, developers can better understand the needs of cybersecurity, information governance, and legal discovery professionals. This insight may allow them to fine-tune large language models to address better the unique challenges and requirements of the eDiscovery ecosystem, ultimately leading to more useful AI-generated outputs for these professionals.
Encouraging transparency in AI development
Highlighting the inclusion of eDiscovery-centric resources in the C4 Dataset emphasizes the importance of transparency in AI development. By understanding the data sources used to train large language models, professionals in the eDiscovery ecosystem may be able to evaluate the reliability of AI-generated outputs better and make more informed decisions about their adoption and integration into their work and workflows.
Conclusion
This high-level exploration of selected eDiscovery-centric resources in the Google C4 Dataset has meaningful implications for professionals in the eDiscovery ecosystem. Analyzing the representation of selected resources in the dataset may help identify potential biases and limitations, enhance the quality and diversity of data used to train large language models, and encourage transparency in AI development. It may also highlight, with context, resources that may have more influence than you would think on shaping LLM-driven answers to prompts and queries. As large language models continue to evolve and become more integrated into the eDiscovery ecosystem, understanding their data sources and potential limitations will be crucial in ensuring their successful application and adoption.
*Assisted by GAI and LLM Technologies
Article References
Additional Reading
Source: ComplexDiscovery
