Summary of Embedllm: Learning Compact Representations Of Large Language Models, by Richard Zhuang et al.
EmbedLLM: Learning Compact Representations of Large Language Models
by Richard Zhuang, Tianhao Wu, Zhaojin Wen, Andrew Li, Jiantao Jiao, Kannan Ramchandran
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 EmbedLLM framework efficiently evaluates and utilizes hundreds of thousands of language models available on Huggingface by learning compact vector representations that facilitate downstream applications. The encoder-decoder approach learns these embeddings, which outperform prior methods in model routing accuracy and latency. Additionally, the method can forecast a model’s performance on multiple benchmarks without additional inference cost. EmbedLLM captures key model characteristics through probing experiments, making it suitable for various tasks such as model routing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EmbedLLM is a new way to use language models more efficiently. It takes hundreds of thousands of language models and turns them into compact versions that can be used quickly and accurately for many different tasks. This makes it better than previous methods at things like predicting which model will do well on certain jobs. The EmbedLLM framework also shows that it can capture important details about each model, even if it wasn’t trained to do specific tasks. |
Keywords
» Artificial intelligence » Encoder decoder » Inference
