Data for LLMs: Navigating the LLM Data Pipeline

Table of Contents

· How to think of data labeling for LLMS
· Basic data considerations for LLM quality
· Data Sharding
· Data Streaming
· Components of Data Flywheel

LLMs (Large Language Models) are all about data. We have had many architectures (eg; the transformers) that disrupted the Machine Learning space especially when dealing with languages in the pre-LLM era. But LLMs have confidently proven that billions of data combined with billions of parameters can easily come with astounding reasoning abilities if combined with similar architectures from before. Generating such high-quality output also requires extensive work on the training data and pipelines that help efficiently process them. Hence, before jumping into model deployment, this article will help you consider the necessities of the data and embedding pipeline.

How to think of data labeling for LLMs

LLMs, like the previous generative models, use decoder architecture. The significance of data labeling in decoder-only architectures of LLMs stems from their autoregressive nature, where the model generates the next token based on the previous sequence of tokens.

In decoder-only architectures, data labeling only requires labeling the output text (unlike encoder-decoder architectures, which require labeling both input and output), which is significantly easier and more efficient. This is because the decoder learns the representation of the input text from the output text.

Example: Given the input “The quick brown fox jumps over the lazy,” the model would only need to predict the next word “dog” without necessarily learning from the two-way context mapping of the encoder-decoder structure.

In a decoder-only model, the ‘labeling’ is inherently dependent on the context provided by the input sequence. The model learns to utilize immediate and extended contextual cues to make accurate predictions, reinforcing its understanding of language structure, syntax, and semantics.

To see a demonstration, you can refer to the Alpaca dataset, specifically curated and structured for instruction tuning for language models.

{
    "instruction": "Create a classification task by clustering the given list of items.",
    "input": "Apples, oranges, bananas, strawberries, pineapples",
    "output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
    "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}

Basic data considerations for LLM quality:

Data Quality: This refers to how accurate, complete, and reliable the data is. For large language models, high data quality means the text they learn from is well-written, diverse, and representative of the language in a broad and comprehensive way. High-quality data helps the model learn correctly and perform well when generating text or understanding language input.

Lilac AI is one tool that focuses on enhancing AI data quality by providing tools that enable users to search, quantify, and edit datasets effectively, particularly for large language models. Its features support data curation processes such as semantic searching, clustering, and detecting and editing fields related to personally identifiable information, duplicates, and language attributes.

Eliminating Data Bias: This is about the unfair or unrepresentative tendencies in the data. If the data used to train LLMs is biased, it means some opinions, perspectives, or types of information are overrepresented or underrepresented. This can cause the model to generate biased output, favoring certain views or demographics over others, and potentially leading to unfair or unethical outcomes.

Privacy and Ethical Considerations: Ensuring data privacy and adhering to ethical standards is crucial, especially when dealing with personal or sensitive information. This includes anonymizing data, securing user consent where necessary, and adhering to legal and ethical guidelines for data usage.

Data Sharding

Data sharding is a method of horizontally partitioning data across multiple machines or servers, known as “shards” or “nodes,” to improve scalability, performance, and availability in large-scale data-intensive systems. Sharding is commonly used in large distributed databases, data warehouses, and data lakes to manage massive volumes of data and support high-concurrency workloads.

Data sharding can be a useful technique for managing and processing large corpora of text data used for training and inference. Here are some ways data sharding can be applied to LLMs:

1. Data partitioning: Divide the training data into smaller, more manageable chunks based on specific criteria, such as document length, topic, or time. This can help reduce the computational requirements for training LLMs and improve parallelism during training.
2. Distributed training: Train LLMs across multiple nodes or machines, with each node handling a specific shard of the data. This can help distribute the computational load and reduce training times.
3. Data balancing: Ensure that each shard of data contains a roughly equal amount of data to prevent any one node from becoming a bottleneck during training or inference.
4. Data replication: Replicate data across multiple nodes for fault tolerance and availability. This can help ensure that the LLM remains available and performant even if one or more nodes fail.
5. Data versioning: Maintain multiple versions of the data shards to support model versioning and experimentation. This can help researchers and developers track the performance of LLMs over time and compare different model architectures and hyperparameters.

Data Streaming

Data streaming, in a general sense, refers to the continuous flow of data being transferred and processed, often in real-time, between different parts of a computer system or network. In the context of machine learning and data processing, data streaming becomes particularly vital as it allows for the efficient handling of large volumes of information, enabling processes to receive and analyze data as it arrives rather than waiting for large batches to be compiled.

In the realm of large-scale machine learning, managing and processing these vast datasets efficiently is crucial. As datasets grow in size and complexity, the limitations of traditional data handling methods become increasingly apparent, particularly when training models across distributed systems or in cloud environments. MosaicML’s StreamingDataset addresses these challenges by optimizing data streaming for machine learning, focusing on aspects like minimizing latency, network inefficiencies, data corruption, and costs across distributed training environments.

For example, when dealing with data stored on remote servers or cloud platforms like S3, the efficiency of transferring only the necessary data, avoiding redundant downloads, and ensuring the data’s integrity becomes paramount. In distributed training contexts, where multiple nodes may require access to the same data, StreamingDataset’s approach ensures that each node accesses only its required subset, thereby reducing network load and avoiding bottlenecks.

Components of Data Flywheel

The data flywheel outlines the flow of data through the training and deployment of LLMs. It consists of five key components: data acquisition, storage, training, deployment, and feedback.

1) Data Acquisition

Generalized text data:

Webpages:

• Content Variety: Data scraped from several diverse web pages.
• Example Datasets: CommonCrawl, ClueWeb datasets, or the GDELT Project.
• Quality Variance: The spectrum ranges from authoritative content to less reliable text, necessitating sophisticated data-cleaning techniques.

Conversational Text:

• Utility: Such data is essential for training LLMs in dialogue understanding and generation.
• Example Datasets: PushShift.io Reddit corpus, Corpus of Linguistic Acceptability (CoLA), transcriptions from the Santa Barbara Corpus of Spoken American English.
• Data Structuring: Organizing this data into logical dialogue sequences is crucial for effective model training, addressing the contextual understanding crucial for interactive applications like chatbots.

Specialized text data

Multilingual Text:

• Objective: To enhance the model’s proficiency across languages.
• Example Datasets: The Tatoeba Project, OPUS.
• Cross-Linguistic Competence: Training on such datasets aids in superior performance across translation and multilingual content creation.

Scientific Text:

• Specialized Knowledge: This data aids in developing domain-specific expertise.
• Sources: arXiv, textbooks, PubMed Central, Semantic Scholar Corpus.
• Technical Adaptation: Processing such data often requires specialized tokenization to effectively capture scientific terminology and concepts.

Code:

• Technical Insight: Code datasets foster LLMs’ understanding and generation of programming languages.
• Sources: Stack Exchange, GitHub, sources like the CodeSearchNet Challenge, or Google’s BigQuery public datasets provide diverse coding materials, from snippets to full programs, alongside commentary and documentation.
• Programming Proficiency: Training on such datasets enables models to better comprehend and generate syntactically correct and logically sound code.

2) Data Storage, training and deployment

For training Large Language Models (LLMs) in the cloud, leveraging robust and scalable storage solutions is essential to manage the massive volumes of training data, as well as training checkpoints and models. Here’s a synthesis of the latest insights into cloud storage solutions tailored for LLM pre-training:

1. AWS Solutions for LLMs: AWS provides a comprehensive environment to facilitate LLM training, offering solutions like Amazon S3 for data lakes and Amazon FSx for Lustre to enhance data processing speeds. These services integrate seamlessly with AWS’s data lakes, ensuring efficient data management and accessibility for LLM training, which is crucial for handling large datasets and supporting Retrieval-Augmented Generation (RAG) processes​.
2. Google Cloud Platform (GCP) Pipelines: GCP facilitates LLM training with examples like finetuning models, demonstrating efficient data preprocessing and training optimization. Their pipeline examples show the integration of various Google Cloud services to streamline LLM training, offering insights into managing data storage, preprocessing, and model training at scale.
3. Anyscale’s Cloud Infrastructure: Anyscale emphasizes flexible and cost-effective storage for LLM and Generative AI applications, ensuring that developers can balance flexibility, cost, and performance. It highlights the importance of finding optimal compute resources, utilizing spot instances to manage costs effectively, and ensuring data privacy through strategic infrastructure choices.
4. Alluxio’s High-Performance Data Access: For distributed training across multiple clouds, Alluxio offers a high-performance data access layer that optimizes model training times and GPU utilization. This system enables efficient data throughput, essential for keeping GPUs optimally utilized during LLM training, and supports model deployment across different cloud environments, ensuring rapid model updates and high availability.