Summary of Xl3m: a Training-free Framework For Llm Length Extension Based on Segment-wise Inference, by Shengnan Wang et al.
XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference
by Shengnan Wang, Youhui Bai, Lin Zhang, Pingyi Zhou, Shixiong Zhao, Gong Zhang, Sen Wang, Renhai Chen, Hua Xu, Hongwei Sun
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed XL3M framework enables large language models (LLMs) trained on short sequences to reason extremely long sequences without further training or fine-tuning. This is achieved by decomposing the input context into multiple short sub-contexts, measuring the relevance between each segment and a common “question,” and constructing a concise key context for inference tasks. The framework’s effectiveness is demonstrated through evaluations on comprehensive benchmarks, showcasing its superiority over existing methods. The XL3M framework has the potential to greatly expand the application of LLMs in scenarios with streaming long inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have difficulty generalizing to texts longer than their maximum training length, limiting their use in scenarios with streaming long inputs. To address this issue, researchers propose a new framework called XL3M that enables large language models trained on short sequences to reason extremely long sequences without further training or fine-tuning. This is achieved by breaking down the input context into smaller segments and measuring how relevant each segment is to a common question. The most relevant segments are then combined to form a concise key context, which is used for inference tasks instead of the original context. |
Keywords
* Artificial intelligence * Fine tuning * Inference
