ParaICL: Towards Robust Parallel In-Context Learning

arXiv:2404.00570v1 Announce Type: new
Abstract: Large language models (LLMs) have become the norm in natural language processing (NLP), excelling in few-shot in-context learning (ICL) with their remarkable abilities. Nonetheless, the success of ICL largely hinges on the choice of few-shot demonstration examples, making the selection process increasingly crucial. Existing methods have delved into optimizing the quantity and semantic similarity of these examples to improve ICL performances. However, our preliminary experiments indicate that the effectiveness of ICL is limited by the …

abstract arxiv become context cs.cl examples few-shot in-context learning language language models language processing large language large language models llms making natural natural language natural language processing nlp norm process processing robust success type