Ada-Instruct: Adapting Instruction Generators for Complex Reasoning (2024.findings-emnlp)
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| Challenge: | Existing methods for generating instructions from a few initial samples with in-context learning are lacking in generating complex instructions of length 100. |
| Approach: | They propose an adaptive instruction generator developed through fine-tuning that generates long, intricate, and distributionally consistent instructions. |
| Outcome: | The proposed method generates long, intricate, and distributionally consistent instructions with ten examples. |
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Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
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| Challenge: | Large language models can perform unseen tasks by conditioning on a few input-output demonstrations, but task inference is implicit and the ability of models to explicitly reason about it remains unexplored. |
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| Challenge: | Large language models (LLMs) have been trained to follow instructions for many NLP tasks, including several tasks from computational argumentation (CA), the computational analysis and synthesis of natural language arguments. |
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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
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CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions (2024.emnlp-main)
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| Challenge: | Current studies have focused on fine-tuning, but the use of instruction tuning is not as effective as fine-cuning. |
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