Summary Extending Context Window of Large Language Models arxiv.org
8,591 words - PDF document - View PDF document
One Line
The document discusses how Position Interpolation can extend the context window of large language models up to 32768 tokens, outperforming direct fine-tuning and maintaining model architecture, with evaluation on benchmark tasks and long document summarization.
Slides
Slide Presentation (12 slides)
Key Points
- Position Interpolation (PI) is a method to extend the context window sizes of large language models (LLMs) up to 32768 tokens with minimal fine-tuning.
- PI down-scales the input position indices to match the original context window size, avoiding high attention scores that disrupt the self-attention mechanism.
- Extending the context window of LLMs is necessary for tasks like long conversations, summarizing long documents, and long-term planning.
- Fine-tuning existing pre-trained Transformer models with longer context windows is inefficient, while PI enables context window extensions for pre-trained LLMs.
- Empirical results show that Position Interpolation is highly effective and efficient, requiring only a short period of fine-tuning for the model to fully adapt to greatly extended context windows.
Summaries
55 word summary
The document explores Position Interpolation (PI) as a method to extend the context window of large language models (LLMs) up to 32768 tokens. PI outperforms direct fine-tuning (FT) and maintains model architecture. Extended models are evaluated on benchmark tasks and long document summarization, showing competitive performance. An appendix provides proof and code snippets for visualizations.
177 word summary
The document "Extending Context Window of Large Language Models" explores a method called Position Interpolation (PI) for extending the context window of large language models (LLMs). PI allows for context window extensions of up to 32768 tokens with minimal fine-tuning. It down-scales the input position indices to match the original context window size, ensuring stability and preserving the original architecture of the models. The need for extending the context window arises from applications such as long conversations, summarizing long documents, and long-term planning.
The authors compare PI to direct fine-tuning (FT) and show that LLMs extended with PI achieve better performance and can effectively leverage longer context windows. They evaluate the extended models on benchmark tasks and long document summarization, demonstrating their competitive performance. The authors conclude that PI can effectively extend the context window of LLMs, making them suitable for a variety of tasks on extended context windows while preserving the quality of the original models.
The summary also mentions an appendix in the document that provides a proof and code snippets used to create visualizations.
251 word summary
This summary is about the document "Extending Context Window of Large Language Models." The document explores methods for extending the context window of large language models (LLMs). The authors introduce a method called Position Interpolation (PI) that allows for context window extensions of up to 32768 tokens with minimal fine-tuning. PI down-scales the input position indices to match the original context window size, ensuring stability and preserving the original architecture of the models. The need for extending the context window arises from applications such as long conversations, summarizing long documents, and long-term planning.
The authors compare PI to direct fine-tuning (FT) and show that LLMs extended with PI achieve better performance and can effectively leverage longer context windows. In contrast, LLMs extended with FT show minimal improvement in the effective context window size. The authors evaluate the extended models on benchmark tasks and long document summarization, demonstrating their competitive performance. They compare their results with existing baselines and highlight the versatility of their method compared to retrieval-augmented LLMs and models with memory capabilities.
The authors conclude that Position Interpolation can effectively extend the context window of LLaMA models, making them suitable for a variety of tasks on extended context windows while preserving the quality of the original models. They also suggest that Position Interpolation could be applicable to other types of LLMs with learnable position embeddings.
In addition to the main content, the summary mentions an appendix in the document that provides a proof and code snippets used to create visualizations.
801 word summary
Position Interpolation (PI) is introduced as a method to extend the context window sizes of large language models (LLMs) such as LLaMA models. PI allows for context window extensions of up to 32768 tokens with minimal fine-tuning, while maintaining strong empirical results on tasks that require long context. PI linearly down-scales the input position indices to match the original context window size, rather than extrapolating beyond the trained context length, which can lead to high attention scores that disrupt the self-attention mechanism. The method is theoretically shown to be stable and the extended models retain their original architecture and can reuse existing optimization and infrastructure. The need for extending the context window of LLMs arises from applications such as long conversations, summarizing long documents, and long-term planning. Training LLMs from scratch with longer context windows is costly, so the question of extending the context window of existing pre-trained models arises. Fine-tuning an existing pre-trained Transformer model with a longer context window has been found to be inefficient, as it results in slow adaptation to long context windows. Techniques such as ALiBi and LeX enable length extrapolation of Transformers, but they have limited applicability for extending the context window sizes of certain pre-trained LLMs. Position Interpolation is introduced as a solution to enable context window extensions for existing pre-trained LLMs. Instead of extrapolating the position indices, PI directly down-scales them so that the maximum position index matches the previous context window limit. This allows for more input tokens by interpolating the position encodings at neighboring integer positions. The interpolated position encodings are easier for the model to adapt to. Empirical results show that Position Interpolation is highly effective and efficient, requiring only a short period of fine-tuning for the model to fully adapt to greatly extended context windows. Experimental results demonstrate that Position Interpolation enables very long context windows, generates strong models, and preserves model quality for tasks within the original context window sizes. The method is compared to direct extrapolation and shown to be much more stable. The paper also discusses the possibility of reducing the interpolation/extrapolation bound through regularization techniques. Experimental results show that models extended with Position Interpolation achieve improved perplexity on long sequence language modeling tasks. The models effectively make use of longer context windows to predict next tokens and show consistent improvements in perplexity with longer context windows. Models extended via direct fine-tuning show limited capability in making use of longer context windows. Some
The study focuses on extending the context window of large language models (LLMs) to improve their performance. The authors evaluate the effectiveness of two methods: Position Interpolation (PI) and direct fine-tuning (FT). They measure the models' performance using perplexity and a passkey retrieval task. The results show that LLMs extended with PI achieve better performance and can effectively leverage longer context windows. In contrast, LLMs extended with FT show minimal improvement in the effective context window size. The authors also evaluate the extended models on benchmark tasks and long document summarization, demonstrating their competitive performance. They compare their results with existing baselines and highlight the versatility of their method compared to retrieval-augmented LLMs and models with memory capabilities. The study contributes to the understanding of extending context windows in LLMs and provides insights for improving their performance in various tasks.
This summary presents key points from the document "Extending Context Window of Large Language Models." The document discusses methods for extending the context window of large language models (LLMs). The authors note that previous methods for extending context windows, such as length extrapolation and interpolation, have not been applied to some of the largest language models, including LLaMA and OPT.
The authors propose a method called Position Interpolation, which extends the context window of LLMs using minimal fine-tuning. They explain that their method is compatible with most existing methods for extending context windows, as it only modifies position encodings and not attention mechanisms. The authors report successful results in extending the context window up to 32 times using Position Interpolation.
The authors compare their method to a similar technique proposed by Dosovitskiy et al. for Vision Transformers. They highlight three main differences: their method interpolates position indices instead of embeddings, they explore a larger upper limit of context window extension, and they confirm the effectiveness of Position Interpolation for extending context windows for language models.
The authors conclude that Position Interpolation can effectively extend the context window of LLaMA models, making them suitable for a variety of tasks on extended context windows while preserving the quality of the original models. They also suggest that Position Interpolation could be applicable to other types of LLMs with learnable position embeddings.
In addition to the main content, the summary includes an appendix that provides a proof and code snippets used to create visualizations in the document.