The MiniPile Challenge for Data-Efficient Language Models (2304.08442v1)

Abstract: The ever-growing diversity of pre-training text corpora has equipped LLMs with generalization capabilities across various downstream tasks. However, such diverse datasets are often too large for academic budgets; hence, most research on Transformer architectures, training procedures, optimizers, etc. gets conducted on smaller, homogeneous datasets. To this end, we present The MiniPile Challenge, where one pre-trains a LLM on a diverse text corpus containing at most 1M documents. MiniPile is a 6GB subset of the deduplicated 825GB The Pile corpus. To curate MiniPile, we perform a simple, three-step data filtering process: we (1) infer embeddings for all documents of the Pile, (2) cluster the embedding space using $k$-means, and (3) filter out low-quality clusters. To verify MiniPile's suitability for LLM pre-training, we use it to pre-train a BERT and T5 model, yielding a performance drop of only $1.9\%$/$2.5\%$ on the GLUE and SNI benchmarks compared to the original pre-trained checkpoints trained on $2.6$x/$745$x the amount of data. MiniPile is available at https://huggingface.co/datasets/JeanKaddour/minipile.

The MiniPile Challenge for Data-Efficient LLMs

The paper "The MiniPile Challenge for Data-Efficient LLMs" by Jean Kaddour addresses a significant concern in contemporary machine learning research: the economic and resource-heavy demands of large-scale LLM pre-training. The challenge presented aims to bridge the gap between high-performance LLMs trained on vast datasets and the resource-constrained scenarios commonly faced by academic researchers.

Introduction and Motivation

The Pile, an 825GB dataset of diverse text, has proven valuable in pre-training LLMs. Despite its utility, the sheer size of The Pile creates barriers for researchers with constrained computational resources. Utilizing smaller, homogeneous datasets such as enwik8 or WikiText103, while economically viable, has been shown to result in substantial performance drops in downstream tasks, exemplified by degraded performance on the GLUE benchmark when training BERT models on such corpora.

The MiniPile Dataset

MiniPile is introduced as a practical compromise, offering a curated, diverse subset of The Pile limited to 1 million documents and 6GB of uncompressed data. The curation process leverages a three-step data reduction framework:

1. Embedding Extraction: Utilizing the E5-Large embedding model to infer embeddings for all documents.
2. Clustering: Employing $k$-means clustering with cosine distance for creating semantically distinct clusters.
3. Human-guided Filtering: Excluding clusters deemed low-quality or harmful based on human assessment.

Performance Validation

To validate the efficacy of the MiniPile dataset, the paper presents empirical results from pre-training BERT and T5 models on MiniPile. The performance drop observed when comparing models pre-trained on MiniPile to those pre-trained on substantially larger datasets is surprisingly minimal. Specifically, the BERT and T5 models exhibited modest performance reductions of 1.9% on GLUE and 2.5% on SNI benchmarks, respectively. Such results underscore the dataset's suitability for pre-training LLMs in resource-constrained settings.

Detailed Methodology

Pruning the Pile

The data reduction strategy is explicitly documented. Key points include:

• The use of batchified $k$-means clustering with careful monitoring of cluster quality.
• Rational exclusion of specific clusters, such as those containing near-duplicate documents, pornography, navigation bars, and long lists of named entities, to prevent performance degradation and avoid ethical concerns.

Experimental Setup

The experiments provided strong evidence supporting the utility of MiniPile. The BERT model, pre-trained using a MLM objective, and the T5 model, pre-trained with span corruption, both demonstrated respectable performances on standard benchmarks. The training protocols adhered to common practices, ensuring results that researchers could readily compare against existing literature.

Discussion and Implications

The strong numerical results position MiniPile as an attractive alternative for data-efficient LLM research. The dataset's diversity and the thorough curation process enable researchers to explore sophisticated models and training techniques without necessitating enormous computational resources. The relatively small drop in performance highlights the potential of domain-specific dataset filtering techniques to provide high-quality training data.

Future Directions

The paper suggests several potential avenues for future research leveraging MiniPile, such as exploring new architectural designs, pre-training schemes, optimization techniques, and privacy-preserving methods. Additionally, the dataset's curated nature serves as an ideal testbed for mechanistic interpretability studies.

Conclusion

The introduction of MiniPile fills a critical gap by providing a feasible, resource-efficient alternative for pre-training LLMs. The dataset fosters equitable research opportunities and proposes a model where high-quality, diverse, yet compact datasets support advanced ML research. This work sets the stage for ongoing and future investigations into more efficient use of data in the training of LLMs, potentially driving both theoretical and practical advancements in the field.

In summary, the MiniPile dataset offers a benchmark for data-efficient LLM research that balances diversity with resource constraints, thereby significantly contributing to democratizing AI research.

References

The paper draws on a wealth of pertinent literature, underscoring the evolution of pre-training datasets, data quality concerns, and related recent advancements in the field. This extensive groundwork provides a comprehensive context for the introduction and validation of MiniPile.