Summary of Resonance Rope: Improving Context Length Generalization Of Large Language Models, by Suyuchen Wang et al.
Resonance RoPE: Improving Context Length Generalization of Large Language Models
by Suyuchen Wang, Ivan Kobyzev, Peng Lu, Mehdi Rezagholizadeh, Bang Liu
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. A novel approach, Resonance RoPE, is introduced to refine the interpolation of RoPE features for OOD positions, improving model performance without additional online computational costs. The paper also presents PosGen, a synthetic benchmark designed for fine-grained behavior analysis in TSTL scenarios. Experiments on synthetic tasks show that applying Resonance RoPE improves Transformer recognition of OOD positions. Extensive LLM experiments demonstrate superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and various downstream long-text applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand longer texts by improving how they recognize words in different positions. The problem is that these computers were trained on shorter texts and struggle with longer ones. The solution is a new way of using something called Rotary Position Embedding (RoPE) to help the computer understand where words are in relation to each other. This makes it better at recognizing words in longer texts. |
Keywords
» Artificial intelligence » Embedding » Token » Transformer
