Summary of Understanding Llm Embeddings For Regression, by Eric Tang et al.
Understanding LLM Embeddings for Regression
by Eric Tang, Bangding Yang, Xingyou Song
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper investigates the application of large language models (LLMs) for regression tasks by preprocessing string representations into LLM embeddings as downstream features for metric prediction. The authors demonstrate that LLM embeddings can be better than traditional feature engineering for high-dimensional regression tasks, and explain this performance in part due to the preservation of Lipschitz continuity over the feature space. Additionally, the paper quantifies the contribution of different model effects, including model size and language understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use big language models to do better with predicting numbers based on strings. They show that using these language models as features can be helpful for tasks where you have lots of data, and explain why this might be the case by saying that the language models help keep things smooth in a special way. They also look at how different aspects of the language model affect its performance. |
Keywords
» Artificial intelligence » Feature engineering » Language model » Language understanding » Regression
