用大模型+BERT结合，零样本识别对话意图并判别越界内容
Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
- 融合BERT的高效计算与大模型泛化能力，实现零/少样本场景下的意图识别
- 在多方对话数据集上，共享BERT输出信息使系统性能提升
- 适合资源有限但需高泛化能力的对话系统研发者
意图识别是任务导向对话系统（TODS）的核心组件。准确识别用户意图并判断其是否超出范围（OOS），对系统提供可靠响应至关重要。然而传统TODS需要大量标注数据。本文提出一种混合方法，结合BERT与大语言模型（LLMs），在零样本和少样本设置下实现意图识别与OOS检测。该方法利用大模型的泛化能力及BERT的计算效率。我们在多方对话语料库上评估该方法，发现将BERT输出信息传递给大模型可有效提升系统性能。
原文摘要 · Abstract (English)
Intent recognition is a fundamental component in task-oriented dialogue systems (TODS). Determining user intents and detecting whether an intent is Out-of-Scope (OOS) is crucial for TODS to provide reliable responses. However, traditional TODS require large amount of annotated data. In this work we propose a hybrid approach to combine BERT and LLMs in zero and few-shot settings to recognize intents and detect OOS utterances. Our approach leverages LLMs generalization power and BERT's computational efficiency in such scenarios. We evaluate our method on multi-party conversation corpora and observe that sharing information from BERT outputs to LLMs leads to system performance improvement.
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