2023 InstructionTuningforLargeLangua
- (Zhang, Dong et al., 2023) ⇒ Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang. (2023). “Instruction Tuning for Large Language Models: A Survey.” doi:10.48550/arXiv.2308.10792
Subject Headings: Instruction Tuning.
Notes
- It provides a detailed survey on Instruction Tuning (IT) for enhancing Large Language Models (LLMs).
- It systematically reviews methodologies, dataset construction, training procedures, and applications of IT.
- It highlights the importance of instruction-output pairs in training datasets for IT.
- It discusses the impact of various factors on IT's effectiveness, including dataset size and output generation techniques.
- It addresses potential pitfalls and critiques of IT, offering a balanced view of its capabilities and limitations.
- It proposes future research directions to overcome current challenges and expand IT's applicability.
- It covers IT applications across different domains and modalities, illustrating the versatility of instruction-tuned models.
- On Instruction Tuning:
- Instruction Tuning enhances the performance and controllability of Large Language Models by training them with datasets of instruction-output pairs.
- Instruction Tuning is critical for improving models' understanding of complex instructions, enabling more accurate and contextually appropriate responses.
- Instruction Tuning involves systematic methodology review, including dataset construction, model training, and diverse applications in various domains.
- Instruction Tuning addresses the generation of outputs that closely follow given instructions, emphasizing the quality and relevance of model responses.
- Instruction Tuning explores the scalability of instruction datasets and their impact on the effectiveness of the tuning process.
- Instruction Tuning identifies potential challenges and limitations within the IT paradigm, suggesting areas for future research and development.
- Instruction Tuning spans different modalities and domains, showcasing its versatility and potential in enhancing model applicability.
Cited By
Quotes
Abstract
his paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a [[dataset consisting of \ textsc{ (instruction]], output) } pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications, along with an analysis on aspects that influence the outcome of IT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research. Project page: this http URL
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 InstructionTuningforLargeLangua | Fei Wu Jiwei Li Shuhe Wang Xiaofei Sun Xiaoya Li Tianwei Zhang Guoyin Wang Shengyu Zhang Linfeng Dong Sen Zhang Runyi Hu | Instruction Tuning for Large Language Models: A Survey | 10.48550/arXiv.2308.10792 | 2023 |