Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (2023.emnlp-main)
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| Challenge: | Existing data for instruction-tuning are inadequate for a wide range of tasks, limiting the scope for nuanced comprehension and interactions within these domains. |
| Approach: | They propose to use Large Language Models to explore a multitude of variations or possibilities to improve instruction-tuning data by active exploration. |
| Outcome: | The proposed approach improves domain-specific instruction coverage and shows significant improvements over baselines. |
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| Challenge: | Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. |
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
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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: | Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data. |
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| Challenge: | InstructEval is a general text evaluator based on open-source Large Language Models (LLMs). |
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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He
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| Challenge: | Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored. |
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
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RECOST: External Knowledge Guided Data-efficient Instruction Tuning (2024.findings-acl)
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| Challenge: | Considering the high computing power overhead, data-efficient instruction tuning is proposed to reduce the training data size. |
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