  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 combining a fine-tuned smaller model
with a retrieval-augmented mechanism, integrated within the architecture of
LLMs. This integration allows LARA to dynamically utilize past dialogues and
relevant intents, thereby improving the understanding of the context.
Furthermore, our adaptive retrieval techniques bolster the cross-lingual
capabilities of LLMs without extensive retraining and fine-tune. Comprehensive
experiments demonstrate that LARA achieves state-of-the-art performance on
multi-turn intent classification tasks, enhancing the average accuracy by 3.67%
compared to existing methods.
