An Intent Classification Method Based on Zero-Shot Context Association Driven by Large Language Models

TAO Hanqing; CHENG Yuhu; WANG Xuesong; WANG Jun

doi:10.12263/DZXB.20250863

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An Intent Classification Method Based on Zero-Shot Context Association Driven by Large Language Models

PAPERS | 更新时间：2026-06-04

- An Intent Classification Method Based on Zero-Shot Context Association Driven by Large Language Models
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 219-233(2026)
- 作者机构：
  
  中国矿业大学信息与控制工程学院，江苏徐州 221116
- 作者简介：
- 基金信息：
  
  China Postdoctoral Science Foundation(2024M753519;GZC20241922);Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB721)
- DOI：10.12263/DZXB.20250863
  CLC： TP181;TP183
- Received：28 September 2025，
  
  Accepted：06 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
陶汉卿, 程玉虎, 王雪松, 等. 大语言模型驱动下基于零样本语境联想的意图分类[J]. 电子学报, 2026, 54(01): 219-233.

TAO Hanqing, CHENG Yuhu, WANG Xuesong, et al. An Intent Classification Method Based on Zero-Shot Context Association Driven by Large Language Models[J]. Acta Electronica Sinica, 2026, 54(01): 219-233.
陶汉卿, 程玉虎, 王雪松, 等. 大语言模型驱动下基于零样本语境联想的意图分类[J]. 电子学报, 2026, 54(01): 219-233. DOI：10.12263/DZXB.20250863

TAO Hanqing, CHENG Yuhu, WANG Xuesong, et al. An Intent Classification Method Based on Zero-Shot Context Association Driven by Large Language Models[J]. Acta Electronica Sinica, 2026, 54(01): 219-233. DOI：10.12263/DZXB.20250863

摘要

意图分类是自然语言处理领域中的一项基础而关键的任务，其目标在于准确识别用户输入语句所表达的潜在意图，是对话系统、智能客服与人机交互等应用的重要技术支撑。近年来，基于深度学习的意图分类方法取得了显著进展，但其性能高度依赖大规模标注语料与稳定的领域分布，在实际应用中仍面临诸多挑战。尤其在短文本信息稀疏、标签语义抽象以及领域先验不足等低资源情境下，用户表达往往具有信息密度低、语义依赖隐含、表述方式多样等特点；同时，意图标签本身通常具有高度抽象性，不同标签之间语义边界模糊，现有模型难以仅凭文本内部的字面特征充分刻画深层语义与语境关联，进而制约了意图分类模型在低资源与跨场景条件下的泛化能力与鲁棒性。针对上述问题，本文从语义扩展与语境建模的角度出发，尝试突破传统监督学习对显式标注样本与表层字面特征的依赖。不同于将任务直接设定为零样本意图分类，本文在有监督学习框架下引入大语言模型的零样本语境联想能力，利用其蕴含的丰富世界知识与语义推理能力，扩展可学习的语义空间，从而弥补文本信息稀疏与标签语义不足所带来的建模缺陷。基于这一思路，本文提出一种基于大语言模型的零样本语境联想模型（LLM-based Zero-shot Context Association Model，L-ZCAM）。该模型通过构造结构化提示词，引导大语言模型从联想意图与标签定义两个互补视角生成与输入语句相关的补充性语境语义信息，实现文本内部特征与文本外部知识的联合挖掘，并对意图标签的语义内涵进行显式增强。在模型结构设计上，L-ZCAM采用多路特征编码与交叉注意力机制，对原始文本特征、联想语义特征及标签语义特征进行深度交互建模；同时，引入约束引导的联合损失函数，对联想语义与标签语义之间的一致性进行约束，以缓解语义噪声带来的干扰，实现文本内外信息的有效对齐。通过上述设计，L-ZCAM能够更好地感知多义模糊、标签抽象以及表达多样等复杂语境下的语义关联关系，从而提升意图判别的准确性与稳定性。实验结果表明，在CLINC150、Banking77和HWU64三个公开数据集上，L-ZCAM的宏平均F1分数分别较当前最新方法提升2.25%、1.28%和1.29%，在不同任务场景下具有更强的泛化能力与鲁棒性。

Abstract

Intent classification is a fundamental and critical task in natural language processing

aiming to accurately identify the underlying intentions expressed in user utterances. It serves as an essential technical foundation for dialogue systems

intelligent customer service

and human-computer interaction. In recent years

deep learning-based approaches have achieved remarkable progress in intent classification; however

their performance heavily relies on large-scale annotated corpora and stable domain distributions

which poses significant challenges in real-world applications. In low-resource scenarios characterized by sparse short-text information

abstract label semantics

and insufficient domain prior knowledge

user expressions often exhibit low information density

implicit semantic dependencies

and diverse surface forms. Meanwhile

intent labels are typically highly abstract with blurred semantic boundaries

making it difficult for existing models to capture deep semantic representations and contextual associations solely from literal textual features. These issues severely limit the generalization ability and robustness of intent classification models under low-resource and cross-domain settings. To address these challenges

this paper explores intent classification from the perspective of semantic expansion and contextual modeling

aiming to reduce the reliance of traditional supervised learning methods on explicit annotations and shallow lexical features. Unlike approaches that directly formulate the task as zero-shot intent classification

we introduce the zero-shot contextual association capability of large language models into a supervised learning framework. By leveraging the rich world knowledge and semantic reasoning ability encoded in LLMs

the proposed approach expands the learnable semantic space

thereby alleviating the modeling limitations caused by sparse textual information and insufficient label semantics. Based on this idea

we propose an LLM-based zero-shot context association model (L-ZCAM). The model constructs structured prompts to guide LLMs to generate complementary contextual semantic information related to the input utterance from two complementary perspectives: associative intents and label definitions. This design enables joint mining of in-text features and out-of-text knowledge while explicitly enhancing label semantics. From a structural perspective

L-ZCAM adopts multi-branch feature encoders and a cross-attention mechanism to deeply model the interactions among original textual features

associative semantic features

and label semantic features. In addition

a constraint-guided joint loss function is introduced to enforce semantic consistency between associative semantics and label semantics

mitigating the impact of semantic noise and achieving effective alignment between internal and external information. Through these designs

L-ZCAM is able to better capture semantic associations under complex contexts involving polysemy

abstract labels

and diverse expressions

thereby improving the accuracy and stability of intent prediction. Experimental results on three public datasets

i.e.

CLINC150

Banking77

and HWU64

demonstrate that L-ZCAM outperforms state-of-the-art methods by 2.25%

1.28%

and 1.29% in terms of macro-averaged F1 score

respectively

exhibiting stronger generalization ability and robustness across different task scenarios.

关键词

Keywords

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