Context Length Scaling in Large Language Models (LLMs)
Scaling context lengths in LLMs presents a multifaceted challenge involving architectural constraints, training efficacy, computational complexity, and financial costs. Addressing these challenges requires innovative approaches that extend beyond traditional fine-tuning methods, paving the way for more versatile and efficient language models capable of processing longer sequences.
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LLMs) such as LLaMA and GPT-4 rely on a fixed context window size. This limits the number of previous tokens they can consider when predicting the next token. Positional embeddings play a crucial role in these models by providing a sense of sequence order. However, scaling these embeddings to accommodate longer sequences presents significant challenges.
Background: Weight Matrix Shapes and Input Tokens
In the architecture of the Transformer model, the sizes of the learnable weight matrices are independent of the number of input tokens. This architectural design implies that the model does not require structural modifications to process more…
