Training Large Language Models on Neurally Compressed Text: Challenges and Opportunities

Keskeiset käsitteet

Training large language models (LLMs) directly over highly compressed neural text can confer advantages in training and serving efficiency, as well as easier handling of long text spans. However, strong compression tends to produce opaque outputs that are not well-suited for learning by standard LLMs. The authors propose a novel compression technique called Equal-Info Windows that enables effective learning over neurally compressed text, outperforming byte-level baselines on perplexity and inference speed benchmarks.

Tiivistelmä

The paper explores the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural text compressors can achieve much higher rates of compression. Training LLMs directly over neurally compressed text could provide benefits in training and serving efficiency, as well as easier handling of long text spans.
The main challenge is that strong compression tends to produce opaque outputs that are not well-suited for learning by standard LLMs. The authors find that text naïvely compressed via Arithmetic Coding is not readily learnable by LLMs.
To overcome this, the authors propose Equal-Info Windows, a novel compression technique that segments text into blocks that each compress to the same bit length. This enables effective learning over neurally compressed text, with the best-performing setting using short 16-bit windows that each correspond to a single 16-bit token. Despite the high compression rate, this approach outperforms byte-level baselines on perplexity benchmarks for fixed computation budgets.
The authors also show that text compressed using GZip is learnable by standard LLMs, but not competitive with their approach. They provide extensive analysis on the properties that contribute to learnability, and offer suggestions for further improving the performance of high-compression tokenizers.
While the authors' best models underperform subword baselines, they demonstrate that learning over neural-compressed text can be effective. The authors discuss the potential advantages of their approach, including increased training and inference efficiency, as well as the ability to model longer-range dependencies.

Tilastot

Compression using Arithmetic Coding alone results in text that is not learnable by standard LLMs, with models only predicting a uniform distribution over tokens.
Compression using a static unigram model as the "modeling" component also fails to produce learnable text.
The authors' proposed Equal-Info Windows compression method, which resets the compression algorithm at fixed bit thresholds, enables effective learning over neurally compressed text.
The best-performing Equal-Info Windows setting uses 16-bit windows, achieving 5.3x token-level compression while outperforming byte-level baselines on perplexity benchmarks.

Lainaukset

"Training LLMs over compressed text is appealing for many reasons. We discuss three advantages in detail below."
"It is not at all obvious what types of compression are "transparent" enough to be learnable through a standard LLM training process."
"To aid learnability, we propose compression via Equal-Info Windows, a simple technique that breaks text into contiguous windows and compresses them via Arithmetic Coding independently."

Tärkeimmät oivallukset

Training LLMs over Neurally Compressed Text

by Brian Lester... klo arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03626.pdf

Syvällisempiä Kysymyksiä

How could the authors' approach be extended to handle variable-length windows or overlapping windows, and what impact might this have on learnability and compression performance

To extend the approach to handle variable-length windows, the authors could consider implementing a dynamic window size based on the complexity of the text being compressed. By allowing the window size to adapt to the information content of the text, the model could potentially achieve better compression rates while maintaining learnability.
On the other hand, overlapping windows could provide a different perspective on the text, allowing the model to capture more nuanced patterns and dependencies. However, overlapping windows might introduce redundancy in the compressed text, potentially affecting the compression ratio. This could be mitigated by carefully designing the overlap size to balance between capturing additional information and maintaining compression efficiency.
Both variable-length and overlapping windows could impact learnability by providing the model with different contexts to learn from. Variable-length windows could help the model adapt to the varying complexity of different parts of the text, while overlapping windows could enhance the model's ability to capture intricate relationships between tokens. However, these approaches may also introduce additional complexity in training and inference, requiring careful optimization to balance compression performance and learnability.

What other techniques, beyond Equal-Info Windows, could be explored to make neural text compression more amenable to learning by large language models

Beyond Equal-Info Windows, several other techniques could be explored to enhance the learnability of neural text compression by large language models:

Adaptive Compression Algorithms: Developing adaptive compression algorithms that can dynamically adjust the compression process based on the input text complexity could improve learnability. These algorithms could optimize the compression rate while ensuring that the compressed text remains informative and learnable by the LLM.

Hybrid Compression Models: Combining multiple compression techniques, such as Arithmetic Coding with Huffman Coding or LZ77, could leverage the strengths of each method to achieve better compression rates and enhance learnability. By integrating different algorithms, the model could benefit from diverse compression strategies.

Context-Aware Compression: Implementing compression algorithms that consider contextual information beyond the immediate token could help preserve long-range dependencies in the compressed text. By incorporating contextual cues during compression, the model could retain essential information for the LLM to learn effectively.

Multi-Stage Compression: Using a multi-stage compression approach where the text undergoes successive rounds of compression with different algorithms could potentially improve the overall compression rate while maintaining learnability. Each stage could focus on different aspects of the text to optimize the final compressed output.

Given the potential advantages of training LLMs over compressed text, how might this approach impact the broader landscape of language model architectures, training techniques, and applications

Training LLMs over compressed text has the potential to revolutionize the landscape of language model architectures, training techniques, and applications in several ways:

Efficient Model Training: By training LLMs over compressed text, the training process becomes more efficient as the model processes more information per token. This efficiency can lead to faster training times, reduced computational costs, and improved scalability for training large models.

Enhanced Long-Range Dependencies: Compressing text to enable LLMs to model longer contextual dependencies can significantly improve the model's performance on tasks requiring understanding of complex relationships across text spans. This capability opens up new possibilities for applications in natural language understanding, summarization, and question-answering tasks.

Adaptive Inference and Latency Reduction: Models trained over compressed text can generate responses with fewer autoregressive steps, reducing latency in inference. This can be crucial for real-time applications where quick responses are essential, such as chatbots, virtual assistants, and search engines.

Diverse Model Architectures: The approach of training LLMs over compressed text could inspire the development of new model architectures optimized for processing compressed input. These architectures could be tailored to leverage the unique characteristics of compressed text, leading to more efficient and effective language models.

Broader Applications: The ability to train LLMs over compressed text opens up opportunities for deploying language models in resource-constrained environments, such as edge devices and low-power systems. This approach could democratize access to advanced language processing capabilities across various domains and industries.