Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches
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 single-GPU fine-tuning of models up to 8B parameters, we combine 4-bit model quantization with Low-Rank
The rapid development and widespread adoption of Large Language Models necessitate more efficient fine-tuning methods, especially for resource-constrained environments, making this research timely.
This research provides practical methodologies for optimizing LLM performance for specific tasks like text classification on limited hardware, democratizing access to advanced AI capabilities.
Fine-tuning of LLMs for text classification can become more accessible and cost-effective, reducing the computational burden previously associated with deploying these models.
- · AI developers with limited compute
- · Small-to-medium enterprises
- · On-device AI applications
- · Researchers
- · Companies reliant on large-scale data centers for basic LLM fine-tuning
- · Inefficient fine-tuning methodologies
More widespread deployment of specialized casual LLMs for text classification tasks.
Increased adoption of LLMs in edge computing and embedded systems due to reduced resource requirements.
The development of a more diverse ecosystem of fine-tuned language models tailored for niche applications, leading to further innovation in AI services.
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Read at arXiv cs.AI