Deep Neural Model Instruction-Tuning Task
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A Deep Neural Model Instruction-Tuning Task is a model fine-tuning task that involves refining a Large Language Model's ability to follow instructions more accurately by training it on a dataset of instructions and their desired outcomes.
- Context:
- It can (typically) involve Model Fine Turning Data (of instructions and the corresponding correct responses or actions).
- It can (often) leverage existing model predictions and human feedback to generate the instruction-response pairs needed for tuning.
- It can result in the model learning to generate more accurate, relevant, and contextually appropriate responses to various instructions.
- It can involve using "self-instruct" approaches to improve response accuracy and relevance through iterative refinements.
- It can (typically) be solved by a Model Instruction-Tuning System (that implements a instruction-tuning algorithm).
- ...
- Example(s):
- Counter-Example(s):
- A standard Model Fine-Tuning task that focuses solely on adjusting model weights based on a specific dataset without using instruction-response pairs.
- A Transfer Learning task where a model is adapted to a new domain without explicitly training it to follow instructions more accurately.
- See: Bootstrapping, Large Language Model, Model Fine-Tuning, Transfer Learning, Visual Instruction Tuning.
References
2024
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Large_language_model#Instruction_tuning Retrieved:2024-2-29.
- Using "self-instruct" approaches, LLMs have been able to bootstrap correct responses, replacing any naive responses, starting from human-generated corrections of a few cases. For example, in the instruction "Write an essay about the main themes represented in Hamlet," an initial naive completion might be 'If you submit the essay after March 17, your grade will be reduced by 10% for each day of delay," based on the frequency of this textual sequence in the corpus.[1]
- ↑ Cite error: Invalid
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2024
- (Liu et al., 2024) ⇒ H Liu, C Li, Q Wu, and YJ Lee. (2024). “Visual instruction tuning.” In: Advances in Neural Information Processing Systems. [1]
- NOTE: It introduces LLaVA, a method for visually instructing tuning of large language models, demonstrating significant advancements in model performance through targeted instruction tuning.
2023
- https://medium.com/neuml/instruction-tune-models-using-your-own-data-with-txtinstruct-3008d8c8d025
- NOTES:
- It refines a Large Language Model's ability to accurately interpret and respond to specific instructions by incorporating datasets tailored to unique requirements, enhancing privacy and specificity.
- It optimizes model responses for domain-specific contexts, thereby ensuring relevance and precision in fields requiring specialized knowledge.
- It systematically generates datasets that align with precise instructions, leveraging both existing model predictions and human feedback to create accurate instruction-response pairs.
- It enhances model output accuracy by anchoring responses to verified data sources, reducing errors and improving trustworthiness.
- It promotes the use of both open-source materials and proprietary data, facilitating a more inclusive and adaptable instruction tuning process.
- It necessitates the careful selection and iterative training of models, focusing on efficiency and relevance to ensure models can follow complex instructions accurately.
- It provides a comprehensive framework for instruction tuning, simplifying the process from dataset creation to model integration, thereby making advanced model customization accessible to a broader audience.
2023
- (Longpre et al., 2023) ⇒ S Longpre, L Hou, T Vu, A Webson, HW Chung, … (2023). “The flan collection: Designing data and methods for effective instruction tuning." In: arXiv preprint arXiv. Provided
- NOTE: It provides a comprehensive overview of instruction tuning methods and introduces the flan collection, aimed at enhancing the effectiveness and research of instruction tuning.
2023
- (Peng et al., 2023) ⇒ B Peng, C Li, P He, M Galley, J Gao. (2023). “Instruction tuning with gpt-4." In: arXiv preprint arXiv:2304.03277. [2]
- NOTE: It explores the application of instruction tuning techniques on GPT-4, highlighting the progress and potential of instruction tuning in improving open-source large language models.
2023
- (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
- NOTES:
- It provides a survey on the field of instruction tuning for large language models, emphasizing its importance in enhancing models' capabilities and controllability through detailed analysis and examples.
- 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.
- NOTES:
2023
- (Liu et al., 2023) ⇒ H Liu, C Li, Y Li, and YJ Lee. (2023). “Improved baselines with visual instruction tuning." In: arXiv preprint arXiv:2310.03744. [3]
- NOTE: It presents advancements in visual instruction tuning, showcasing improved baselines and best performance achievements, and discusses the challenges and solutions in fine-tuning visual models.