KaFT: Knowledge-aware Fine-tuning for Boosting LLMs’ Domain-specific Question-Answering Performance (2025.findings-acl)
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| Challenge: | Recent literature reveals that supervised fine-tuning (SFT) is suboptimal for domain-specific question-answering tasks. |
| Approach: | They propose a query diversification strategy for robust conflict detection and a knowledge-aware fine-tuning approach to effectively boost LLMs’ performance. |
| Outcome: | The proposed approach improves the model generalization and alleviates the hallucination. |
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| Challenge: | Large Language Models (LLMs) can be fine-tuned on task-specific data to improve performance on target tasks but can be overfitted resulting in a loss of generalization. |
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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods reduce the number of trainable parameters while maintaining strong downstream performance. |
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| Challenge: | Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts. |
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| Challenge: | Large Language Models (LLMs) are composed of neurons that exhibit diverse behaviors and roles. |
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| Challenge: | Recent advances in large language models (LLMs) have greatly improved natural language understanding and generation. |
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| Challenge: | Existing studies suggest that the order of training samples can affect model performance, but this is not the case. |
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| Challenge: | Recent work attributes optimization instability to the low probability of demonstrations being incompatible with the sample level. |
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