Beyond More Context: Retrieval Diversity Boosts Multi-Turn Intent Understanding

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 (JGA) under equal token budgets, surpassing strong LLM/DST baselines, with consistent improvements across K= 4 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.