LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning (AI summary)

Key Points

- The paper presents a novel method called SelfExtend, which aims to extend the context window of pretrained language models (LLMs) without the need for fine-tuning.

- The motivation for SelfExtend arises from the belief that LLMs should have inherent capabilities to handle long contexts without the need for extensive fine-tuning.

- LLMs encounter challenges when faced with text sequences longer than their pretraining context window, leading to unpredictable behaviors.

- The proposed SelfExtend method constructs bi-level attention information, including grouped attention and neighbor attention, to enable LLMs to handle long contexts without fine-tuning.

- Comprehensive experiments on multiple benchmarks demonstrate that SelfExtend effectively extends the context window length of existing LLMs.

- The paper highlights the Out-of-Distribution (O.O.D) issues related to positional encoding as a key challenge preventing LLMs from effectively handling longer contexts.

- SelfExtend is demonstrated to outperform fine-tuning-based models on synthetic and real-world long context tasks, highlighting its superior capabilities.

- The study also explores the impact of varying group sizes and neighbor window sizes on the performance of SelfExtend, revealing important trade-offs and considerations for optimal performance.

- Future work is proposed to test SelfExtend on models using other positional encoding and more challenging tasks, along with the consideration of more sophisticated mapping methods for achieving better long context understanding abilities.

Summary

The research paper discusses the limitations of existing large language models (LLMs) due to the fixed context window length during pretraining, leading to unpredictable behavior and performance degradation when faced with long input sequences at the inference stage. Various context window extension methods are explored, including fine-tuning and more efficient approaches. However, these methods typically require fine-tuning, leading to resource and time-intensive processes. The paper challenges the assumption that pretrained LLMs lack the ability to handle long content and focuses on addressing the positional Out-of-Distribution (O.O.D) issue as the key challenge for LLMs in effectively handling longer contexts.

The proposed method, SelfExtend, addresses the O.O.D positional encoding issue by remapping unseen large relative positions to those encountered during pretraining, thereby extending the LLMs' ability to handle longer contexts without fine-tuning. The effectiveness of SelfExtend is demonstrated on language modeling and long context tasks, showcasing its potential to surpass existing finetuning-based models and improve long context understanding ability. The paper also discusses the trade-offs associated with varying group size and neighbor window size, highlighting the need for careful consideration when fine-tuning models for handling long contexts. The results showcased the superior capabilities of SelfExtend, demonstrating its potential as a plug-in component for LLMs. The limitations and future work for SelfExtend are also discussed, providing insight into potential improvements and use cases for further research.

Reference: https://arxiv.org/abs/2401.01325