Summary of Fuse-ing Language Models: Zero-shot Adapter Discovery For Prompt Optimization Across Tokenizers, by Joshua Nathaniel Williams et al.
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
by Joshua Nathaniel Williams, J. Zico Kolter
First submitted to arxiv on: 9 Aug 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 The proposed FUSE (Flexible Unification of Semantic Embeddings) approach aims to facilitate knowledge transfer in prompt discovery tasks by approximating an adapter layer that maps between different large language model embedding spaces. This inexpensive method utilizes a third-order tensor-based representation to align semantic embeddings split apart by various tokenizers, enabling the derivation of an approximation of the gradient of one model’s outputs with respect to another model’s embedding space. The efficacy of FUSE is demonstrated through multi-objective optimization over vision-language and causal language models for image captioning and sentiment-based image captioning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FUSE is a new way to help different large language models understand each other better. It’s like a translator that can take words from one model and turn them into the right words for another model, even if they use different ways of breaking down text into tiny pieces. This makes it easier for machines to learn from each other and do cool things like caption images and predict what people will say about those images. |
Keywords
» Artificial intelligence » Embedding space » Image captioning » Large language model » Optimization » Prompt
