用多样性检索提升对话意图理解，不靠加长上下文
Beyond More Context: Retrieval Diversity Boosts Multi-Turn Intent Understanding
- 按意图覆盖和语言多样性筛选示例，平衡信息丰富度
- 在相同字数限制下，联合目标准确率显著优于基线模型
- 适合资源受限场景，对示例数量和顺序变化稳健
多轮对话中的意图理解是任务导向型聊天机器人的核心挑战。实际部署中面临严格的令牌预算和噪声上下文，现有检索流程多关注相关性，忽视集合层面的多样性以及更多上下文或示例顺序等混淆因素。本文探讨在固定预算下，检索多样性是否比延长提示更系统地提升大模型的意图理解能力。提出一种多样性的感知检索框架，通过选择兼顾意图覆盖与语言多样性的上下文示例，并将其与标准大模型解码器集成。评估在保持预算一致、随机化示例位置的条件下进行，包含对示例数量（K=4~7）、多样性强度和模型规模的敏感性分析。在MultiWOZ 2.4和SGD数据集上，该方法在相同令牌预算下实现了联合目标准确率的显著提升，超越多个强基线模型，且性能稳定。研究验证了内容多样性在检索中的独立作用，并为构建高效、可部署的多轮意图理解系统提供了简洁可行的选取原则。
原文摘要 · Abstract (English)
Multi turn intent understanding is central to task oriented chatbots, yet real deployments face tight token budgets and noisy contexts, and most retrieval pipelines emphasize relevance while overlooking set level diversity and confounds such as more context or exemplar order. We ask whether retrieval diversity, rather than longer prompts, systematically improves LLM intent understanding under fixed budgets. We present a diversity aware retrieval framework that selects in context exemplars to balance intent coverage and linguistic variety, and integrates this selection with standard LLM decoders; the evaluation enforces budget matched prompts and randomized positions, and includes sensitivity analyses over exemplar count, diversity strength, and backbone size. On MultiWOZ 2.4 and SGD, the approach achieves strong gains in Joint Goal Accuracy under equal token budgets, surpassing strong LLM/DST baselines, with consistent improvements across K from 4 to 7 and moderate latency. Overall, the study isolates and validates the impact of content diversity in retrieval and offers a simple, deployable selection principle for building accurate, budget constrained multi turn intent systems.
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