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AI Research
arXiv CS.AI·Jun 11·5 min read
System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5
TL;DR
Existing language models often struggle with specific tasks due to limited training data. A new dataset was created for the CCL25-Eval Task 5, and the Qwen2.5 model was fine-tuned using Low-Rank Adaptation (LoRA) techniques.
✦ Why It Matters
Engineers can leverage fine-tuning techniques like LoRA to improve model performance on specialized tasks with limited data.
Key Takeaways
- Explore the CCPoetry-49K dataset for training models on classical poetry.
- Utilize the PoetryQwen model for improved translation tasks.
- Apply Low-Rank Adaptation techniques for fine-tuning LLMs.
- Focus on domain-specific subtasks for better model performance.
How It Works
The study decomposes the poetic appreciation task into three subtasks: term interpretation, semantic interpretation, and emotional inference. This structured approach allows for targeted data collection and model training, leading to the creation of the CCPoetry-49K dataset.
The Qwen2.5-14B model is then fine-tuned using Low-Rank Adaptation (LoRA), which optimizes the model's performance for the specific nuances of classical poetry.