Title: ETT: Expanding the Long Context Understanding Capability of LLMs at Test-Time
Primary area: foundation or frontier models, including LLMs
Keywords: Test Time Training, Long Context Understanding

Abstract: Transformer-based Language Models' computation and memory overhead increase quadratically as a function of sequence length. The quadratic cost poses challenges when employing LLMs for processing long sequences. In this work, we introduce ETT (Extend at Test-Time), method for extending the context length of short context Transformer-based LLMs, with constant memory requirement and linear computation overhead.
ETT enables the extension of the context length at test-time by efficient fine-tuning the model's parameters on the input context, chunked into overlapping small subsequences.
We evaluate ETT on LongBench by extending the context length of GPT-Large and Phi-2 up to 32 times, increasing from 1k to 32k tokens. This results in up to a 30% improvement in the model's accuracy.
We also study how context can be stored in LLM’s weights effectively and efficiently. Through a detailed ablation study, we examine which Transformer modules are most beneficial to fine-tune at test-time. Interestingly, we find that fine-tuning the second layer of the FFNs is more effective than full fine-tuning, leading to a further improvement in the models' accuracy.

TL;DR: We propose ETT, a method for extending the context length of short-context Transformer-based LLMs by fine-tuning on overlapping input chunks at test time, with constant memory and linear computational overhead.