Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches
📰 ArXiv cs.AI
Learn to fine-tune causal LLMs for text classification using embedding-based and instruction-based approaches, and understand their trade-offs under resource constraints
Action Steps
- Fine-tune a pre-trained causal LLM by attaching a classification head and using the final-token embedding as a sequence representation
- Implement instruction-tuning by formatting the classification task as a prompt-to-response problem for the LLM
- Compare the performance of embedding-based and instruction-based approaches on a downstream text classification task
- Evaluate the trade-offs between the two approaches in terms of accuracy, efficiency, and resource requirements
- Apply the chosen approach to a specific text classification task, such as sentiment analysis or topic modeling
Who Needs to Know This
NLP engineers and researchers can benefit from this knowledge to improve the performance of their text classification models, especially when working with limited resources
Key Insight
💡 Embedding-based and instruction-based approaches can be used to fine-tune causal LLMs for text classification, with different trade-offs in terms of accuracy, efficiency, and resource requirements
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Fine-tune causal LLMs for text classification using embedding-based or instruction-based approaches #LLMs #NLP
Key Takeaways
Learn to fine-tune causal LLMs for text classification using embedding-based and instruction-based approaches, and understand their trade-offs under resource constraints
Full Article
Title: Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches
Abstract:
arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource constraints. Two approaches are investigated: (1) attaching a classification head to a pre-trained causal LLM and fine-tuning on the task using the LLM's final-token embedding as a sequence representation, and (2) instruction-tuning the LLM in a prompt-to-response format for classification. To enable sin
Abstract:
arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource constraints. Two approaches are investigated: (1) attaching a classification head to a pre-trained causal LLM and fine-tuning on the task using the LLM's final-token embedding as a sequence representation, and (2) instruction-tuning the LLM in a prompt-to-response format for classification. To enable sin
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