LARA: Linguistic-Adaptive Retrieval-Augmented LLMs for Multi-Turn Intent Classification

arXiv:2403.16504v1 Announce Type: new
Abstract: Following the significant achievements of large language models (LLMs), researchers have employed in-context learning for text classification tasks. However, these studies focused on monolingual, single-turn classification tasks. In this paper, we introduce LARA (Linguistic-Adaptive Retrieval-Augmented Language Models), designed to enhance accuracy in multi-turn classification tasks across six languages, accommodating numerous intents in chatbot interactions. Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. LARA tackles these issues by …

abstract accuracy arxiv augmented llms classification context cs.cl cs.ir however in-context learning language language models large language large language models llms paper researchers retrieval retrieval-augmented six studies tasks text text classification type