Exploring Large Language Model Datasets: A Comprehensive Survey

Core Concepts

The author explores the critical role of datasets in advancing Large Language Models, categorizing them into five perspectives and highlighting challenges and future directions.

Abstract

The paper delves into the importance of datasets for Large Language Models (LLMs), categorizing them into pre-training corpora, instruction fine-tuning datasets, preference datasets, evaluation datasets, and traditional NLP datasets. It emphasizes the significance of high-quality data for model performance and outlines the evolution of text datasets from NLP to LLMs. The survey provides insights into prevailing challenges, future trends, and a comprehensive review of publicly available dataset resources.

Stats

The total data size surveyed surpasses 774.5 TB for pre-training corpora.
Over 700M instances are covered by other datasets.
The corpus BBT-FinCorpus has a size of 256 GB.
FinGLM corpus is 69 GB in size.
Medical-pt corpus is 632.78 MB in size.

Quotes

"The construction and analysis of LLM datasets is an area worthy of attention."
"Without high-quality datasets as the foundation, it is challenging to grow the tree of LLMs with flourishing branches and leaves."

Key Insights Distilled From

Datasets for Large Language Models

by Yang Liu,Jia... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18041.pdf

Deeper Inquiries

How do domain-specific pre-training corpora contribute to enhancing model performance

Domain-specific pre-training corpora play a crucial role in enhancing model performance by providing specialized knowledge and context relevant to specific fields or topics. These corpora allow models to focus on learning domain-specific language patterns, terminologies, and nuances, which are essential for performing well in tasks within that particular domain. By training LLMs on domain-specific data, the models can develop a deeper understanding of the intricacies of the subject matter, leading to improved performance and accuracy in generating text or making predictions related to that domain.
Moreover, domain-specific pre-training corpora help fine-tune models for specific applications or industries, enabling them to excel in tasks such as financial analysis, medical diagnosis, legal document processing, etc. This targeted training ensures that the model is well-equipped with the necessary information and expertise required for handling complex scenarios within a particular field. Overall, leveraging domain-specific pre-training corpora enhances model adaptability and effectiveness when dealing with specialized content or tasks.

What are the implications of evolving text datasets from NLP to LLMs

The evolution of text datasets from traditional Natural Language Processing (NLP) datasets to Large Language Models (LLMs) signifies a significant shift towards more extensive scales of data collection and utilization. The transition reflects advancements in deep learning techniques and computational capabilities that have enabled researchers to train large-scale language models effectively.
With NLP datasets focusing on fundamental linguistic tasks like semantic analysis and machine translation since the 1960s-1980s era until recent times emphasizing dialogue systems and multilingual datasets; LLM datasets represent an exponential growth in scale complexity diversity challenges faced by researchers today.
The implications of this evolution include:

Scale: LLM datasets are significantly larger than traditional NLP datasets due to their need for vast amounts of unlabeled text data during pre-training.
Complexity: LLM datasets incorporate diverse sources like webpages social media academic materials code parallel corpus encyclopedia creating richer contextual understanding.
Diversity: LLM datasets cover multiple languages domains reflecting real-world scenarios ensuring robustness across various applications.
Challenges: Researchers face issues related dataset quality timeliness selection preprocessing necessitating novel approaches ensure effective use these expansive resources.

Overall evolving text datasets from NLP to LLMs mark a paradigm shift towards more comprehensive inclusive versatile data collections supporting advanced AI research applications.

How can researchers address challenges related to dataset quality and diversity

Researchers can address challenges related dataset quality diversity through several strategies aimed at improving overall dataset efficacy reliability:

Quality Assessment: Implement rigorous evaluation mechanisms assess dataset quality relevance ensuring only high-quality data used training validation processes.

Data Preprocessing: Conduct thorough cleaning filtering deduplication standardization review processes enhance dataset cleanliness consistency reducing noise errors biases present raw data.

Diverse Data Sources: Curate diverse range sources webpages books social media encyclopedias academic materials code parallel corpus ensure broad coverage different types texts contexts enriching model training experience.

4 .Collaborative Efforts: Collaborate experts professionals specific domains create curated labeled annotated subsets improve dataset richness accuracy tailored requirements downstream tasks
5 .Continuous Improvement: Regularly update refine existing datasets incorporate new information trends maintain relevancy adaptability changing environments needs ensuring long-term usability effectiveness
By adopting these strategies researchers can overcome challenges associated with dataset quality diversity fostering enhanced performance robustness AI models trained using these varied comprehensive textual resources