Tree Prompting: Efficient Task Adaptation without Fine-Tuning (2310.14034v1)

Abstract: Prompting LMs is the main interface for applying them to new tasks. However, for smaller LMs, prompting provides low accuracy compared to gradient-based finetuning. Tree Prompting is an approach to prompting which builds a decision tree of prompts, linking multiple LM calls together to solve a task. At inference time, each call to the LM is determined by efficiently routing the outcome of the previous call using the tree. Experiments on classification datasets show that Tree Prompting improves accuracy over competing methods and is competitive with fine-tuning. We also show that variants of Tree Prompting allow inspection of a model's decision-making process.

• The paper introduces Tree Prompting, which constructs decision trees from LM prompts to enable task-specific adaptation without fine-tuning.
• It demonstrates a 16.2% accuracy improvement over basic few-shot methods, leveraging dynamic prompt generation for enhanced interpretability.
• The approach efficiently uses a fixed number of LM queries, paving the way for scalable and interpretable predictive modeling.

Tree Prompting: Efficient Task Adaptation without Fine-Tuning

Introduction

The study presented in "Tree Prompting: Efficient Task Adaptation without Fine-Tuning" (2310.14034) provides a novel approach for using LMs to adapt to specific tasks without engaging in traditional fine-tuning processes. Through the construction of decision trees composed of prompts, the methodology offers improvements in accuracy and efficiency in classification tasks. This essay elaborates on the foundational concepts, experimental results, and future implications of Tree Prompting.

Conceptual Framework and Methodology

Tree Prompting innovatively builds a decision tree where each node corresponds to a prompt evaluated by the LM. This design streamlines the process of classification by leveraging the outcomes of previous prompts to inform subsequent decisions, thereby forming a coherent decision-making pathway (Figure 1).

Figure 1: Illustration of Tree Prompting where a subset of training data is used to prompt the LM at each node to partition the input space.

The primary method for determining prompts involves sampling few-shot examples from training datasets, a strategy inspired by bagging predictors. The prompts are converted into binary decision splits using verbalizers—functional mappings of LM outputs into discrete actions. This decision tree structure translates fine-tuning data into sparse, yet effective, models that only necessitate a fixed number of LM queries during inference.

Experimental Results and Analysis

Classification Accuracy

Experiments on 13 datasets demonstrate the prowess of Tree Prompting over conventional few-shot and ensemble prompting methods. Notably, Tree Prompting consistently outperformed its counterparts in terms of accuracy across different LM sizes and datasets. Results indicate an average accuracy improvement over basic few-shot methods by 16.2% when using smaller models like GPT-2 Small. However, stability was noted to be less compared to gradient fine-tuning, yet Tree Prompting excelled in specific tasks, highlighting its efficacy in maximizing classification performance through tree structures (Figure 2).

Figure 2: Performance as a function of the number of LM evaluations per example (#LM calls), showing Tree Prompting’s efficiency.

Interpretability and Dynamic Prompts

An advantageous feature of decision trees lies in their capacity for interpretability. Through Tree Prompting, researchers can inspect decision nodes for insights on model predictions. Additionally, dynamic prompt generation at nodes offers adaptability and enhanced understanding of the underlying decision process.

Figure 3: Tree Prompting tree with dynamic prompts showing the decision-making process on MR dataset.

Comparison with kNN Prompting

Compared against kNN Prompting, Tree Prompting showcased superior performance on most datasets by utilizing rigorous decision pathways expressed through trees rather than relying solely on nearest-neighbor aspects. This integration provides more flexibility in representing the input space without predefined verbalizer constraints.

Figure 4: Example tree for the MR dataset demonstrating the efficacy of Tree Prompting using dynamic prompt selection.

Implications and Future Directions

The Tree Prompting approach proposes an adaptable framework for leveraging LMs without the computationally expensive gradient-based fine-tuning. The efficiency of inference and adaptability to large LMs through repeated prompt evaluations presents advantageous applications, particularly in resource-centric environments. Additionally, future avenues explore generalization capabilities to broader tasks beyond text classification, potential improvements in decision-tree algorithms, and integration of diverse computational constraints for dynamic node navigation.

Tree Prompting’s capacity to merge prompt generation and tree structural learning offers a scalable and interpretative method for predictive modeling using LMs. Future research can explore modular configurations, extending its application across tasks such as function calling in programming or optimizing traversals within complex decision trees to further enhance efficiency and interpretability.

Conclusion

In summary, Tree Prompting offers a significant shift in how LMs are utilized for task-specific adaptations. Providing an efficient alternative to fine-tuning, the research stands as both a paradigm and a practical tool for machine learning applications. It encourages further exploration into modular decision frameworks, paving the way for more refined and interpretable task adaptation methods within AI technologies.