Summary of Found in the Middle: How Language Models Use Long Contexts Better Via Plug-and-play Positional Encoding, by Zhenyu Zhang et al.
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
by Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao, Olatunji Ruwase, Beidi Chen, Xiaoxia Wu, Zhangyang Wang
First submitted to arxiv on: 5 Mar 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 This paper addresses the “lost-in-the-middle” challenge in large language models (LLMs), which struggle to identify relevant information within long contexts. To overcome this limitation, the authors introduce Multi-scale Positional Encoding (Ms-PoE), a simple and effective plug-and-play approach that enhances LLMs’ capacity for middle-context understanding without fine-tuning or additional overhead. Ms-PoE leverages position index rescaling to relieve the long-term decay effect introduced by RoPE, while assigning distinct scaling ratios to attention heads to preserve pre-trained knowledge. The authors demonstrate the efficacy of Ms-PoE through extensive experiments with various LLMs, achieving an average accuracy gain of up to 3.8 on the Zero-SCROLLS benchmark. This paper’s contributions include a novel approach for handling middle-context information and improved performance in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines understand long pieces of text better. Right now, big language models struggle to find important details in the middle of long texts. To fix this problem, the authors created a new way to help these models, called Multi-scale Positional Encoding (Ms-PoE). This approach makes it easier for models to identify important information without needing extra training or complicated calculations. The authors tested Ms-PoE with different language models and found that it improved their performance by up to 3.8%. This new way of helping language models will make them better at understanding text, which can be useful in many applications. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Positional encoding
