This paper introduces the groundbreaking Infini-gram, which modernizes traditional n-gram language models by leveraging trillion-token training data. It challenges historical constraints on n, introducing the concept of an ∞-gram LM and demonstrating its potential to complement neural language models, yielding improved predictive accuracy and efficiency. The paper outlines Infini-gram’s implications and applications across diverse neural LMs, offering diverse possibilities from text analysis to copyright infringement mitigation.
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Groundbreaking Approach to Scale and Enhance N-Gram Models Beyond Traditional Limits
Introduction
Pretrained on trillion-token corpora, large neural language models (LLMs) have achieved remarkable performance strides. However, the scalability benefits of such data for traditional n-gram language models (LMs) still need to be explored. This paper delves into the relevance of n-gram LMs in the era of neural LLMs and introduces groundbreaking advancements in their modernization.
Modernization of N-Gram LMs
The authors modernized traditional n-gram LMs by scaling training data to an unprecedented 1.4 trillion tokens, representing the largest n-gram LM to date. They introduce the concept of an ∞-gram LM, with unbounded n, utilizing a backoff variant for improved accuracy.
Efficiency and Implementation
The ∞-gram LM leverages a suffix array, achieving remarkable efficiency with low-latency, resource-efficient querying. The paper outlines efficient methods for n-gram counting, occurrence position retrieval, and document identification, reducing latency and optimizing processing times.
Application and Impact
Infini-gram’s application across diverse neural LMs demonstrates consistent perplexity improvements, showcasing its efficacy in complementing neural LMs across different model series. The paper establishes a positive correlation between neural LMs and ∞-gram, suggesting the latter’s potential to enhance LM performance in predicting human-written text.
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