Summary of Beyond the Limits: a Survey Of Techniques to Extend the Context Length in Large Language Models, by Xindi Wang et al.
Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models
by Xindi Wang, Mahsa Salmani, Parsa Omidi, Xiangyu Ren, Mehdi Rezagholizadeh, Armaghan Eshaghi
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| 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 survey reviews recent techniques and methods designed to extend the sequence length in large language models (LLMs), enhancing their capacity for long-context understanding. The authors categorize a wide range of architectural modifications, such as modified positional encoding and altered attention mechanisms, which aim to process longer sequences without increasing computational requirements. These methodologies can be applied across different phases of LLMs, including training, fine-tuning, and inference. The limitations of current approaches are discussed, along with suggestions for future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to make large language models work better with long sentences. Right now, these models are great at understanding what’s being said, but they get overwhelmed if the sentence is too long. The authors found some new techniques that can help them handle longer sentences without getting bogged down. These ideas can be used in different parts of the model, like when it’s learning or making predictions. This will make language models more helpful for people who need to process lots of information. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Inference * Positional encoding
