Summary of Hierarchical Context Merging: Better Long Context Understanding For Pre-trained Llms, by Woomin Song et al.
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
by Woomin Song, Seunghyuk Oh, Sangwoo Mo, Jaehyung Kim, Sukmin Yun, Jung-Woo Ha, Jinwoo Shin
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel training-free scheme called Hierarchical cOntext MERging (HOMER) to overcome the limitations of large language models (LLMs) in processing long inputs. HOMER uses a divide-and-conquer algorithm, dividing input texts into manageable chunks and then merging adjacent chunks at progressive transformer layers. The approach also employs a token reduction technique to ensure memory usage efficiency. In addition, an optimized computational order is proposed to reduce the memory requirement, which scales logarithmically with respect to input length. This makes HOMER particularly suitable for environments with tight memory restrictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to help large language models process longer texts without running out of memory or needing expensive training. It does this by breaking down long inputs into smaller chunks and then combining these chunks in a special way. This lets the model work on really long texts, which is important for many applications like chatbots and virtual assistants. |
Keywords
» Artificial intelligence » Token » Transformer
