EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·25d ago·5 min read
LLM Classification Is Feature Engineering
TL;DR
Using large language models (LLMs) as classifiers presents challenges, particularly in terms of label calibration and the effective use of structured data. Despite these issues, LLMs can still yield decent performance in classification tasks.
✦ Why It Matters
Evaluate your classification needs to determine if LLMs are the right tool for your structured data tasks.
Key Takeaways
- Use LLM outputs as features in logistic regression models.
- Improve model calibration by estimating parameters from training data.
- Incorporate additional covariates to enhance classification performance.
- Evaluate and refine features based on model residuals.
How It Works
By treating LLM predictions as features in a logistic regression framework, engineers can leverage the strengths of LLMs while ensuring better calibration and interpretability. This method allows for the integration of structured data and additional features, improving overall model performance.