LLM embeddings can outperform traditional feature engineering in high-dimensional regression tasks, with better Lipschitz continuity and smoothness properties
This paper investigates how LLM embeddings can be used for regression tasks, showing they maintain strong performance in high-dimensional spaces while preserving smoothness properties that enable better predictions compared to traditional feature engineering methods.
-----
https://arxiv.org/abs/2411.14708
🤔 Original Problem:
Traditional regression methods struggle with high-dimensional data, requiring complex feature engineering. Using LLM embeddings could offer a solution, but their effectiveness for regression tasks remains unexplored.
-----
🔧 Solution in this Paper:
→ The paper uses LLM embeddings as features for standard regression tasks, processing inputs as strings into fixed vector representations
→ They analyze both T5 and Gemini model families across synthetic and real-world regression tasks
→ A key innovation is measuring embedding smoothness through Normalized Lipschitz Factor Distribution
-----
💡 Key Insights:
→ LLM embeddings maintain performance in high-dimensional spaces where traditional methods fail
→ Embedding smoothness strongly correlates with regression performance
→ Larger models don't always mean better regression results
→ Pre-training benefits vary across tasks, with some showing minimal improvements
-----
📊 Results:
→ On AutoML tasks (29 parameters), LLM embeddings outperformed traditional methods by up to 41.3%
→ T5-XXL showed consistent improvement with size, while Gemini models showed more variance
→ Performance gaps between methods decrease with more training data
Share this post