D.Va: Validate Your Demonstration First Before You Use It

2025-02-19

AI Summary

Existing in-context learning (ICL) demonstration selection methods rely on heuristic metrics, exhibiting poor robustness and limited cross-model generalization. To address this, we propose D.Va (Demonstration Validation), the first framework to introduce a *demonstration validation* perspective for ICL. D.Va dynamically selects demonstrations that are both effective and generalizable through three synergistic components: multi-dimensional effectiveness evaluation, cross-model consistency verification, and joint optimization with retrieval models. Unlike conventional static scoring approaches, D.Va establishes a transferable, validation-driven demonstration selection paradigm. Extensive experiments demonstrate that D.Va consistently outperforms state-of-the-art methods across diverse natural language understanding (NLU) and natural language generation (NLG) benchmarks. Moreover, it exhibits strong robustness and cross-architecture generalization across multiple large language models (LLMs) and retrieval models.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heavily relies on selecting effective demonstrations to generate outputs that better align with the expected results. As for demonstration selection, previous approaches have typically relied on intuitive metrics to evaluate the effectiveness of demonstrations, which often results in limited robustness and poor cross-model generalization capabilities. To tackle these challenges, we propose a novel method, extbf{D}emonstration extbf{VA}lidation ( extbf{D.Va}), which integrates a demonstration validation perspective into this field. By introducing the demonstration validation mechanism, our method effectively identifies demonstrations that are both effective and highly generalizable. extbf{D.Va} surpasses all existing demonstration selection techniques across both natural language understanding (NLU) and natural language generation (NLG) tasks. Additionally, we demonstrate the robustness and generalizability of our approach across various language models with different retrieval models.

Problem

• Enhances in-context learning robustness
• Improves cross-model generalization capabilities
• Validates effective and generalizable demonstrations

Innovation

• Introduces demonstration validation mechanism
• Enhances cross-model generalization capabilities
• Surpasses existing demonstration selection techniques