How Do I Prep My Data To Train An Llm 2

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Blog / How Do I Prep my Data to Train an LLM?

How Do I Prep my Data to Train an LLM? Jacob Solawetz ,

Malikeh Ehghaghi ,

Shamane Siri ,

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July 9, 2024

So you want to train a custom language model, and you do have the requisite large set of text data. But how do you know that the data is *really actually ready* for model training? Our researchers here at Arcee AI tell you what to look out for.

We all know the data adage "Garbage-In, Garbage-Out" – any results you get from your data can only be as good as the data itself. It's a saying that applies, of course, in the world of artificial intelligence: the quality of any AI model depends on the quality of the data that you've fed into it. Here at Arcee AI, every day we talk to organizations that are eager to build, train and deploy custom LLMs (actually, what we call Small Language Models or SLMs – because our models are so efficient). As we get them started on their SLM journey, we start by reminding them – or teaching them – how to properly prepare their text data before using it to train a language model. Some of our brilliant researchers put together this guide of what you need to know as you prep your data to train an SLM or LLM. They've divided their advice into the two main considerations you need to keep in mind: the amount of data you're working with, and the quality of that data. Data Quantity Data quantity plays a pivotal role in shaping the capabilities of Large Language Models (LLMs). The more extensive your dataset, the more nuanced and accurate the understanding your models can achieve. Data quantity empowers language models to generalize effectively across diverse topics and tasks, underpinning their ability to comprehend and generate human-like text. Large-Scale Corpus The pre-training corpus should be large to ensure the model has acquired extensive knowledge . Typically, this involves processing billions or trillions of tokens for pre-training a general-purpose model to capture the complexity and variability of language . Integrating diverse data sources further enhances the effectiveness of pre-training language models. In Continual Pre-training (CPT) tasks, focusing on large-scale domain-specific datasets is crucial. These datasets, whether supervised or unsupervised, play a pivotal role in refining models for specific domains. Interestingly, CPT requires significantly less data than initial pre-training , while still delivering comparable performance. Augmenting with Synthetic Data Acquiring large, diverse, and high-quality datasets is a challenge, often due to data scarcity, privacy concerns, and the high costs of collecting and labeling data. Numerous analysts predict that we will run out of fresh text data by 2050 and image data by 2060 . To tackle these issues, synthetic data has become a promising solution. Augmenting with synthetic data is a complex topic itself, and we'll be publishing an upcoming blog devoted just to that. Data Quality Beyond sheer volume, the quality of data defines the foundation of reliable and effective language models. Ensuring data quality and utilizing filtering techniques to exclude undesirable text are vital for optimizing the performance of the language models. Data Diversity and Distribution In the previous section, we focused on the corpus size, but what if we have a huge corpus that is not capable of demonstrating various patterns and distributions of the language? This brings up the necessity of having a diverse corpus as your input data. More diverse pre-training data enhances the model's ability to acquire a wider range of knowledge – similar to the impact of a large-scale corpus, but different in that the model is exposed to various and broad ranges of possible input data . Including varied data sources can help in developing comprehensive language models and improving the model’s generalizability . Deduplicated Dataset Deduplicating the pre-training data is the process of removing duplicates and redundant data samples, which helps the model by preventing it from memorizing repeated sequences and instead encourages generalization . This procedure enhances models’ robustness against forgetting, and boosts the model’s performance in acquiring factual knowledge . Data Filtering Data filtering is another crucial step that significantly enhances the efficiency of the Continual Pre-Training (CPT) process. By meticulously selecting only the most effective and relevant tokens from the pre-training data, we can reduce the overall computational cost and resource consumption. In Efficient Continual Pre-training for Building Domain Specific Large Language Models , the authors illustrated how strategic data selection can lead to substantial performance gains with minimal data and compute resources. They propose simple yet effective data selection strategies that outperform standard CPT with just 10% of the corpus size and cost, without compromising performance on open-domain tasks. This approach not only ensures that the model focuses on high-quality domain-specific data, but also reduces redundancy and noise in the training process. There are a number of different, filtering techniques and some of the most useful strategies are introduced below. Language Filtering The initial necessary step to collecting pre-training data for language modeling involves filtering data based on the target languages that the model will work with, and filtering out the data from other languages. Language filtering can be applied to both natural language and programming languages.

Content Filtering The content filtering step includes eliminating data that contains toxic, explicit, or extremely inappropriate content to enhance the model’s fairness and safety. While this step can reduce harmful outputs of the model, it might limit the model’s ability to perform well on standard benchmarks and tasks. Thus, there is a trade-off between the generalization ability of the model and removing toxic content from the pre-training dataset to mitigate the risk of toxic content generation. The content in the samples might also leak Personally Identifiable Information (PII) of people. Recent experiments have shown that language models will reproduce PII during inference time. Therefore, filtering out the PII from the collected...