Papers with SemiFT
Semi-supervised Fine-tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs require labeled data, which can be costly in real-world applications. |
| Approach: | They propose a framework that can fully exploit labeled and unlabeled data for LLM fine-tuning . they conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets . |
| Outcome: | The proposed framework can fully exploit labeled and unlabeled data for LLM alignment from a propagate-and-select manner. |