The paper discusses the challenges of training models on longer contexts due to the quadratic scaling of self-attention in memory and compute as context length increases. It reviews various methods proposed for context length extrapolation, and introduces a new approach called truncation. The authors also critique the commonly used next-token perplexity metric for evaluating LLM performance, and introduce new tasks focused on model recall accuracy. The paper concludes by acknowledging the ongoing challenges in context length extrapolation of LLMs, and expresses interest in further research on the topic.
Key takeaways:
- The article introduces Giraffe, a new family of models finetuned from base LLaMA and LLaMA2, with a focus on context length extrapolation in Large Language Models (LLMs).
- Context length extrapolation is crucial for tasks that require the model to attend to a larger corpus of data, such as information retrieval, maintaining long conversations, or coding assistance.
- The paper explores various methods for context length extrapolation, including a new approach called truncation, and introduces new tasks focused on the accuracy of model recall rather than text coherence.
- Despite the advancements, the paper acknowledges that none of the methods fully satisfy the need for true extrapolation without performance degradation, indicating a need for further research in this area.