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Enhancing Large Language Models' Out-of-Demonstration Generalization through Query-Aware Demo Generation

Core Concepts
Large language models can leverage query-aware demo generation to enhance their ability to generalize beyond the provided demonstrations and solve out-of-demonstration queries.
The paper presents a novel prompting method called SELF-DEMOS that aims to elicit the out-of-demonstration (OOD) generalizability in large language models (LLMs). The key idea is to generate query-aware demonstrations that strategically interpolate between existing demonstrations and the given OOD query, transforming the query from OOD to in-demonstration (ID). The authors first construct a dataset called OOD-Toolset, which features tool-using scenarios with real-world APIs and OOD queries that require different sub-APIs compared to the provided seed demonstrations. They then introduce the SELF-DEMOS workflow, which consists of four steps: Query Understanding: The model is prompted to provide a general understanding of the user query, simplifying the complexity of the subsequent analysis. Query-aware Demo Generation: The model generates N demos that strategically interpolate between the seed demos and the given query. Best-of-N Sampling: The model selects the K best demos from the N generated ones based on criteria like accuracy, relevance, and potential helpfulness. Response Generation: The model uses the selected demos along with the seed demos to generate the final response. Extensive experiments on the OOD-Toolset, GSM8K, and MATH datasets show that SELF-DEMOS outperforms state-of-the-art baselines in solving OOD queries. The authors also conduct ablation studies and analyses to validate the effectiveness and generalization of their approach across different model sizes and task complexities.
The OOD-Toolset dataset contains over 300 real-world APIs and 1,057 instances, each with 3 seed demos and 1 OOD query. The GSM8K dataset has 1,319 instances, with manually created outliers for the OOD setting. The MATH dataset has 1,000 instances, with problems from the same level but different subjects for the OOD setting.
"Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations." "When faced with out-of-demonstration (OOD1) queries, methods that rely on hand-crafted demos or external retrievers might fail." "By strategically interpolating, we can derive more relevant and accurate demos from existing ones, which have proven helpful for the final response."

Key Insights Distilled From

by Wei He,Shich... at 04-02-2024

Deeper Inquiries

How can the SELF-DEMOS method be extended to other domains beyond tool-using and mathematical problem-solving?

The SELF-DEMOS method can be extended to other domains by adapting the query-aware demo generation approach to suit the specific requirements of those domains. Here are some ways to extend the method: Domain-specific Prompting: Customize the prompts and demo generation process to align with the characteristics of the new domain. This may involve understanding the unique patterns and structures of the data in that domain and tailoring the demo generation accordingly. Data Preprocessing: Preprocess the data from the new domain to ensure that it is compatible with the SELF-DEMOS method. This may involve cleaning the data, extracting relevant features, and structuring it in a way that the model can effectively learn from it. Task-specific Criteria: Define specific criteria for demo selection that are relevant to the new domain. This could include factors such as accuracy, relevance, and domain-specific constraints that need to be considered during the demo generation and selection process. Evaluation and Fine-tuning: Evaluate the performance of the SELF-DEMOS method in the new domain and fine-tune the parameters and processes based on the results. Iterative refinement and testing will help optimize the method for the specific domain. By adapting the SELF-DEMOS method to different domains in a systematic and tailored manner, it can be effectively applied to a wide range of tasks beyond tool-using and mathematical problem-solving.

What are the potential limitations of the query-aware demo generation approach, and how can they be addressed?

The query-aware demo generation approach, while effective, may have some limitations that need to be addressed: Limited Training Data: If the model has not been exposed to a diverse range of queries and demonstrations during training, it may struggle to generate accurate and relevant demos for new queries. This can be addressed by augmenting the training data with a wider variety of examples. Overfitting: The model may generate demos that are too specific to the training data, leading to overfitting and reduced generalization to new queries. Regularization techniques and data augmentation can help mitigate this issue. Complex Queries: Generating demos for complex or ambiguous queries may pose a challenge for the model. Breaking down complex queries into simpler sub-tasks and guiding the model through step-by-step demo generation can help address this limitation. Bias in Demo Selection: The model may exhibit bias in selecting demos based on the training data, leading to skewed results. Implementing diverse sampling strategies and incorporating feedback mechanisms can help reduce bias in demo selection. By actively addressing these limitations through data augmentation, regularization, task decomposition, and bias mitigation strategies, the query-aware demo generation approach can be enhanced to improve its effectiveness and robustness.

How can the computational overhead of the SELF-DEMOS method be further reduced without compromising its effectiveness?

To reduce the computational overhead of the SELF-DEMOS method while maintaining its effectiveness, the following strategies can be implemented: Optimized Demo Generation: Implement more efficient algorithms and techniques for demo generation to minimize redundant computations and streamline the process. This can include optimizing the prompt templates, leveraging pre-trained models, and parallelizing computations where possible. Caching and Reuse: Utilize caching mechanisms to store and reuse intermediate results, such as key-value vectors, to avoid redundant computations. By reusing previously generated demos and intermediate outputs, the method can reduce the computational cost of repeated tasks. Batch Processing: Process multiple queries and demos in batches to leverage parallel processing and optimize resource utilization. Batch processing can help reduce the overall computational time and improve efficiency. Model Optimization: Fine-tune the model architecture and hyperparameters to strike a balance between computational complexity and performance. By optimizing the model for the specific task and dataset, the computational overhead can be minimized without compromising the quality of the generated demos. By implementing these strategies and continuously optimizing the computational workflow of the SELF-DEMOS method, it can achieve a balance between computational efficiency and effectiveness in generating query-aware demos.