Papers by Tianci Xue
PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning (2024.findings-acl)
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| Challenge: | In-context instruction tuning is a method that fine tunes with supervised instruction data to enhance the instruction following ability of large language models. |
| Approach: | They propose an in-context instruction tuning method that incorporates positive and negative examples into the prompt for better performance. |
| Outcome: | The proposed method outperforms baseline instruction tuning methods on in-domain and out-domain tasks up to 9.16 and 3.14 average ROUGE-L scores. |