Summary of Llm Augmented Llms: Expanding Capabilities Through Composition, by Rachit Bansal et al.
LLM Augmented LLMs: Expanding Capabilities through Composition
by Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A new study proposes CALM (Composition to Augment Language Models), a method that efficiently composes foundational language models with more specific models, enabling the acquisition of new capabilities. By introducing cross-attention between models, CALM allows for the re-use of existing model weights and preserves their original skills while scaling up performance on new tasks. The approach is demonstrated on diverse domains and settings, including low-resource languages and code generation, showing improvements of up to 13% and 40%, respectively. This method has significant implications for the practical application of large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundational models are like super-smart robots that can do many things really well. But sometimes they need a little help to learn new skills. A team of researchers created CALM, a way to make these robots even better by combining them with smaller models that have special expertise. This lets the big model keep its old skills and get new ones too! The scientists tested CALM on some tricky tasks and found it worked really well, especially when helping robots understand languages they didn’t know before or generating code for programming. |
Keywords
* Artificial intelligence * Cross attention
