Demonstration Retrieval

Demonstration retrieval focuses on selecting the most effective examples from a dataset to improve the performance of large language models (LLMs) and other machine learning systems in few-shot learning scenarios. Current research emphasizes developing efficient and effective retrieval methods, often employing techniques like contrastive learning, reinforcement learning, and list-wise ranking to optimize example selection across diverse tasks and model architectures. This area is crucial for enhancing the efficiency and adaptability of LLMs, reducing reliance on extensive fine-tuning, and enabling their application in resource-constrained environments or domains with limited labeled data. The resulting improvements in model performance have significant implications for various applications, including robotics, education, and natural language processing.