Summary of Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap For Prompt-based Large Language Models and Beyond, by Xinyu Wang et al.
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
by Xinyu Wang, Hainiu Xu, Lin Gui, Yulan He
First submitted to arxiv on: 22 Feb 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 Task embeddings, a key technique for meta-learning, have shown promise in areas like multi-task learning, model editing, and interpretability. However, they struggle when large language models (LLMs) operate without gradients. Existing methods rely on fine-tuned language models, limiting adaptability across diverse models, especially prompt-based LLMs. To unlock the potential of task embeddings with LLMs, we propose FUTE (Framework for Unified Task Embeddings), which harmonizes task embeddings from various models, including smaller language models and LLMs with varied prompts, within a single vector space. This enables comparison and analysis of similarities across different models, expanding the scope and utility of existing methods in multi-model scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Task embedding is a way for machines to understand what tasks they’re good at. It’s like having a special ID card that says “I’m great at this job!” But sometimes, when big language models are used, it gets tricky to make sure these IDs work across different models. That’s why we created FUTE, which helps make task embedding work better with all kinds of language models. This means we can compare and learn from different models in a single place, making it easier to understand what makes them special. |
Keywords
* Artificial intelligence * Embedding * Meta learning * Multi task * Prompt * Vector space
