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AI Research
arXiv CS.AI·May 25·5 min read
Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches
TL;DR
Text classification using Large Language Models (LLMs) faces resource constraints. Two fine-tuning methods were explored: embedding-based and instruction-based approaches.
✦ Why It Matters
Engineers can optimize LLM fine-tuning for text classification using efficient methods to reduce resource consumption.
Key Takeaways
- Utilize embedding-based fine-tuning for single-label classification tasks.
- Implement 4-bit quantization to reduce resource usage during training.
- Explore instruction-tuning for multi-label classification with larger models.
- Leverage internal representations of LLMs for improved classification performance.
How It Works
The embedding-based approach fine-tunes a pretrained LLM by adding a classification head that uses the final-token embedding as a representation for the input sequence. This method allows for efficient training by significantly reducing the number of parameters that need to be adjusted, thanks to techniques like LoRA and model quantization.