Summary of Trol: Traversal Of Layers For Large Language and Vision Models, by Byung-kwan Lee et al.
TroL: Traversal of Layers for Large Language and Vision Models
by Byung-Kwan Lee, Sangyun Chung, Chae Won Kim, Beomchan Park, Yong Man Ro
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 introduces a new efficient large language and vision models (LLVM) family, Traversal of Layers (TroL), which enables the reuse of layers in a token-wise manner. TroL simulates the effect of looking back and retracing the answering stream while increasing the number of forward propagation layers without physically adding more layers. The paper demonstrates that TroL efficiently outperforms open-source LLVMs with larger model sizes and rivals the performances of closed-source LLVMs with substantial sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TroL is a new efficient large language and vision models (LLVM) family that helps solve the problem of large models requiring costly, high-end resources for training and inference. This approach reuses layers in a token-wise manner, simulating looking back and retracing the answering stream while increasing the number of forward propagation layers without adding more layers. The paper shows that TroL performs well compared to larger open-source LLVMs and rivals closed-source LLVMs with substantial sizes. |
Keywords
* Artificial intelligence * Inference * Token
