ETT: Expanding the Long Context Understanding Capability of LLMs at Test-Time

• URL: http://arxiv.org/abs/2507.06313v2
• Date: Tue, 15 Jul 2025 13:47:22 GMT
• Title: ETT: Expanding the Long Context Understanding Capability of LLMs at Test-Time
• Authors: Kiarash Zahirnia, Zahra Golpayegani, Walid Ahmed, Yang Liu,
• Abstract summary: ourmodelacronym(Extend at Test-Time) is a method for extending the context length of short context Transformer-based Language Models.<n>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.
• Score: 5.554829574749047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \ourmodelacronym~(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 enable 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 percent 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.

Related papers

• Length Controlled Generation for Black-box LLMs [70.57649832433451]
  Large language models (LLMs) have demonstrated impressive instruction following capabilities, but struggle to accurately manage the length of generated text.<n>We propose a novel iterative sampling framework for text length control, integrating the Metropolis-Hastings algorithm with an importance sampling acceleration strategy.<n>Our framework achieves almost 100% success rates of length control on Llama3.1 for tasks such as length-controlled abstractive summarization.
  arXiv  Detail & Related papers  (2024-12-19T09:07:38Z)
• LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models [72.71150585370147]
  LongRecipe is an efficient training strategy for extending the context window of large language models. It simulates long-sequence inputs while maintaining training efficiency and significantly improves the model's understanding of long-range dependencies. LongRecipe can utilize long sequences while requiring only 30% of the target context window size, and reduces computational training resource over 85% compared to full sequence training.
  arXiv  Detail & Related papers  (2024-08-31T17:19:30Z)
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters [27.656263126925815]
  We study the scaling of inference-time computation in LLMs. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt.
  arXiv  Detail & Related papers  (2024-08-06T17:35:05Z)
• Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training [78.93900796545523]
  Mini-Sequence Transformer (MsT) is a methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input sequences and iteratively processes mini-sequences to reduce intermediate memory usage. integrated with the huggingface library, MsT successfully extends the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x.
  arXiv  Detail & Related papers  (2024-07-22T01:52:30Z)
• Investigating Low-Rank Training in Transformer Language Models: Efficiency and Scaling Analysis [16.253898272659242]
  This study focuses on Transformer-based LLMs, specifically applying low-rank parametrization to feedforward networks (FFNs) Experiments on the large RefinedWeb dataset show that low-rank parametrization is both efficient (e.g., 2.6$times$ FFN speed-up with 32% parameters) and effective during training. Motivated by this finding, we develop the wide and structured networks surpassing the current medium-sized and large-sized Transformer in perplexity and throughput performance.
  arXiv  Detail & Related papers  (2024-07-13T10:08:55Z)
• MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention [36.49445805074941]
  MInference (Milliontokens Inference) is a sparse calculation method designed to accelerate pre-filling of long-sequence processing. We demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy.
  arXiv  Detail & Related papers  (2024-07-02T17:59:56Z)
• Scaling Sparse Fine-Tuning to Large Language Models [67.59697720719672]
  Large Language Models (LLMs) are difficult to fully fine-tune due to their sheer number of parameters. We propose SpIEL, a novel sparse finetuning method which maintains an array of parameter indices and the deltas of these parameters relative to their pretrained values. We show that SpIEL is superior to popular parameter-efficient fine-tuning methods like LoRA in terms of performance and comparable in terms of run time.
  arXiv  Detail & Related papers  (2024-01-29T18:43:49Z)
• E^2-LLM: Efficient and Extreme Length Extension of Large Language Models [74.1254067728251]
  We propose an Efficient and Extreme length extension method for Large Language Models, called E 2 -LLM, with only one training procedure and dramatically reduced cost. Comprehensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our E 2 -LLM on challenging long-context tasks.
  arXiv  Detail & Related papers  (2024-01-13T02:11:20Z)
• PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training [91.99700930388998]
  We propose Positional Skip-wisE training that simulates long inputs using a fixed context window. PoSE greatly reduces memory and time overhead compared with Full-length fine-tuning. We have successfully extended the LLaMA model to 128k tokens using a 2k training context window.
  arXiv  Detail & Related papers  (2023-09-19T08:03:38Z)
• Giraffe: Adventures in Expanding Context Lengths in LLMs [7.8327063299618]
  We show that linear scaling is the best method for extending context length. We also discover promising extrapolation capabilities in the truncated basis. To support further research in this area, we release three new 13B parameter long-context models.
  arXiv  Detail & Related papers  (2023-08-21T17:30:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.