Summary of Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training, by Junqin Huang et al.
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
by Junqin Huang, Zhongjie Hu, Zihao Jing, Mengya Gao, Yichao Wu
First submitted to arxiv on: 11 May 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 Piccolo2 embedding model surpasses other models in a comprehensive evaluation of six tasks on the CMTEB benchmark, setting a new state-of-the-art. The model leverages an efficient multi-task hybrid loss training approach to effectively harness textual data and labels from diverse downstream tasks. Additionally, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Piccolo2 is a new embedding model that does really well on a bunch of different tasks. It’s better than other models at doing these tasks, which is cool! The way it works is by using some special math to look at lots of different pieces of text and labels, and then combining all that information into one place. This helps the model be more flexible and good at lots of different things. |
Keywords
» Artificial intelligence » Embedding » Multi task
