Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation (2402.18334v3)

Abstract: We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. We aim to enable zero-shot task adaptation of LLMs on users' specialized, private data. We train Bonito by fine-tuning a pretrained LLM on a new large-scale dataset with 1.65M examples created by remixing existing instruction tuning datasets into meta-templates. The meta-templates for a dataset produce training examples where the input is the unannotated text and the task attribute and the output consists of the instruction and the response. We use Bonito to generate synthetic tasks for seven datasets from specialized domains with unannotated text across three task types -- yes-no question answering, extractive question answering, and natural language inference -- and adapt LLMs. We show that Bonito significantly improves the average performance of pretrained and instruction tuned models over the de facto self supervised baseline. For example, adapting Mistral-Instruct-v2 and instruction tuned variants of Mistral and Llama2 with Bonito improves the strong zero-shot performance by 22.1 F1 points whereas the next word prediction objective undoes some of the benefits of instruction tuning and reduces the average performance by 0.8 F1 points. We conduct additional experiments with Bonito to understand the effects of the domain, the size of the training set, and the choice of alternative synthetic task generators. Overall, we show that learning with synthetic instruction tuning datasets is an effective way to adapt LLMs to new domains. The model, dataset, and code are available at https://github.com/BatsResearch/bonito.

• The paper introduces Bonito, a new model that auto-generates instruction tuning datasets from unannotated text to enable zero-shot adaptation.
• The methodology leverages meta-templates to produce 1.65M synthetic examples across specialized domains, significantly boosting F1 scores.
• Empirical results show Bonito outperforms self-supervision baselines by up to 39.1 F1 points, demonstrating its scalability and efficiency.

Enhancing Zero-Shot Task Adaptation with Bonito: A Model for Generating Instruction Tuning Datasets

Introduction to Bonito

In the field of LLMs, the quest for models that can adeptly handle tasks in specialized domains through zero-shot task adaptation has led to the development of Bonito. As the introduction of instruction tuning has improved LLMs' ability to generalize to unseen tasks, the limited scope of existing instruction tuning datasets has become apparent. These datasets primarily cover generic tasks, leaving a gap in models' abilities to handle specialized domains. Bonito steps in to address this gap by automating the creation of instruction tuning datasets from unannotated text, specifically targeting specialized domains. This paper details Bonito's development and evaluates its impact on zero-shot task adaptation, positioning it as a significant advancement for adapting LLMs to new domains.

Automating Conditional Task Generation

Bonito is introduced as a model designed for conditional task generation: transforming unannotated text into task-specific training datasets suitable for instruction tuning. This process facilitates the zero-shot adaptation of LLMs to specialized, user-specific data, marking a stride towards personalized model tuning with minimal human intervention. Through training on a novel dataset comprising 1.65M examples generated using meta-templates, Bonito is adept at producing synthetic tasks across multiple specialized domains and task types. Its effectiveness is underscored by its ability to improve the average performance of both pretrained and instructionally-tuned models in specialized domain tasks.

Key Contributions and Results

The paper delineates several pivotal contributions of Bonito, including: the introduction of an open-source model for conditional task generation, empirical evidence of Bonito's superiority over self-supervision baselines, and an in-depth analysis revealing the model's scalability across varying domains and synthetic task generation methods. Notably, Bonito demonstrates a remarkable improvement of up to 39.1 F1 points over self-supervision baselines, highlighting its efficacy in enhancing models' performance through zero-shot task adaptation.

Technical Details and Experimentation

Delving into the technical intricacies, the paper explains the structure and training process of the Bonito model, alongside detailing the setup for generating synthetic tasks. The experiments are meticulously designed to assess Bonito's impact across seven datasets involving three task types: yes-no question answering, extractive question answering, and natural language inference. The results consistently show Bonito's superiority over baseline models, particularly noting its capacity to significantly outperform self-supervised learning methods in terms of F1 score improvements.

Impact and Theoretical Implications

The introduction of Bonito is positioned as a significant advancement in the field of LLMs, primarily for its potential to democratize access to specialized domain models. By enabling effective zero-shot task adaptation without extensive annotated datasets, Bonito paves the way for more tailored and accessible LLM applications across diverse fields. Theoretically, this research contributes to a deeper understanding of instruction tuning's potential when paired with innovative dataset generation methods, challenging existing paradigms of model training and adaptation.

Future Directions

The paper speculates on future advancements in AI, hinting at the exploration of more nuanced task generation models and improved methodologies for synthetic dataset creation. The adaptability of Bonito to varied domains and task types opens avenues for further research into domain-specific LLM adaptations, potentially revolutionizing how models are trained for specialized applications.

Limitations and Ethical Considerations

Acknowledging its limitations, the paper underscores the reliance on vast amounts of unannotated text and the contextual applicability of its findings. Moreover, it highlights potential risks associated with model biases and the generation of factually incorrect datasets, stressing the need for ethical considerations in the deployment of models like Bonito.

Conclusion

Bonito represents a meaningful leap towards enhancing the adaptability of LLMs to specialized domains through the automated generation of instruction tuning datasets. Its proven effectiveness in improving zero-shot task adaptation performance not only marks it as a significant contribution to the field but also lays the groundwork for future explorations aimed at further refining the capabilities of generative AI models.