Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks (2024.emnlp-main)
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| Challenge: | Experimental results show that instruction tuning improves zero-shot generalization across various tasks and improves performance of specific tasks. |
| Approach: | They propose a task selection method that leverages instruction information alone to identify relevant tasks and optimize instruction tuning for specific tasks. |
| Outcome: | The proposed method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. |
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| Challenge: | Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models on diverse tasks with instructions. |
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| Challenge: | Existing studies have shown that instruction tuning is effective for generalizing to arbitrary tasks unseen during training. |
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| Challenge: | Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. |
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| Challenge: | Recent studies on instruction tuning (IT) have achieved great performance with zero-shot generalizability to unseen tasks. |
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| Challenge: | Instruction-tuning improves a model’s ability to learn in-context, but the mechanisms that drive in-constext learning are poorly understood. |
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
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| Challenge: | Existing instruction-tuned models struggle to adhere to a query with multiple intentions, which impairs their performance when the completion of several tasks is demanded by a single command. |
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From Selection to Refinement: Iterative Optimization for Instruction Data (2026.acl-long)
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Hang Hu, Ziyan Liu, Rujie Wen, Ruihui Hou, Xueyan Wu, Mu Zhang, Jianxing Yu, Tong Ruan, Jingping Liu
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Task-aware Retrieval with Instructions (2023.findings-acl)
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Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih
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ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)
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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu
| Challenge: | Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance . |
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