Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples

Abstract

Large Language Models exhibit powerful few-shot in-context learning (ICL) capabilities, but the performance is highly sensitive to provided examples. Recent research has focused on retrieving corresponding examples for each input query, not only enhancing the efficiency and scalability of the learning process but also mitigating inherent biases in manual example selection. However, these studies have primarily emphasized leveraging Positive samples while overlooking the additional information within Negative samples for contextual learning. We propose a novel method that utilizes Negative samples to better select Positive sample examples, thereby enhancing the performance of few-shot ICL. Initially, we construct Positive and Negative sample corpora based on Zero-Shot-Cot. Then, during inference, we employ a semantic similarity-based approach to select the most similar examples from both the Positive and Negative corpora for a given query. Subsequently, we further retrieve Positive examples from the Positive sample corpus based on semantic similarity to the Negative examples, then concatenating them with the previously selected Positive examples to serve as ICL demonstrations. Experimental results demonstrate that our approach surpasses methods solely relying on the most similar positive examples for context, validating that the additional information in negative samples aids in enhancing ICL performance through improved Positive sample selection.