The paper introduces a covariance‑corrected Mahalanobis distance method for detecting out‑of‑domain (OOD) intents in conversational agents. It builds on prior work showing Mahalanobis distance’s effectiveness but addresses its shortcomings in few‑shot scenarios, where limited examples of new intents are available. The authors analyze why the original approach underperforms and propose a refined distance metric to improve OOD detection in practical settings.
By Jayasimha Talur, Oleg Smirnov, Paul Missault
The paper evaluates whether zero‑shot large language models (LLMs) can replace fine‑tuned natural language understanding (NLU) classifiers for intent detection in conversational systems. Experiments on ATIS and CLINC150 show that fine‑tuned RoBERTa outperforms Claude Haiku zero‑shot when ample in‑domain labels are available, but the LLM matches the supervised model on the broader 150‑intent CLINC150 schema. The authors identify three production‑relevant scenarios where LLMs excel—out‑of‑scope detection, robustness to ASR noise, and dynamic per‑deployment schemas—and present a decision framework to guide practitioners.
whyItMatters:"The study provides concrete evidence and a practical framework for deciding when to deploy LLMs versus fine‑tuned models in real‑world conversational applications."
By Carson Rodrigues, Oysturn Vas
arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.
By Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
arXiv:2511. 08378v4 Announce Type: replace-cross Abstract: Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions.
By Xiao Wang, Ke Qin, Dongyang Zhang, Xiurui Xie, Shuang Liang
arXiv:2511. 05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems.
By Hongtao Wang, Renchi Yang, Wenqing Lin
arXiv:2608. 10939v1 Announce Type: cross Abstract: Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet aggregate evaluation often hides large differences between high-resource and low-resource languages.
By Wajdi Ben Saad, Safa Madiouni
arXiv:2405. 12775v2 Announce Type: replace-cross Abstract: Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions.
By Hanlei Zhang, Hua Xu, Fei Long, Xin Wang, Kai Gao
MISApp is a profile‑free framework that predicts the next mobile app a user will launch by learning multi‑hop session graphs. It captures transition dependencies across different structural ranges, incorporates temporal context and spatial categorization, and models intent evolution from recent interactions. Experiments on two real‑world datasets show MISApp outperforms baselines in both standard and cold‑start settings while remaining efficient, and analyses reveal that multi‑hop relations provide higher‑order predictive signals and interpretable attention weights.
By Yunchi Yang, Longlong Li, Jianliang Wu, Cunquan Qu
Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decoding stochasticity, particularly in multi-intent scenarios. In view of this, we propose Semantic Frame-Level Multi-Task Self-Consistency (SFL-MTSC), a novel structured aggregation framework operating at the semantic frame level.
Intent Engine is a natural‑language intent translation architecture that converts user intents into validated Service‑level Objectives (SLOs) for compute‑continuum microservice placement. It combines schema‑constrained extraction, retrieval‑grounded value construction from monitored infrastructure, and validation against supported constraints to produce reliable SLO artifacts. In evaluations on a 716‑record dataset, Intent Engine outperformed prompting baselines and a rule‑based parser, achieving a 0.941 total F1 score with GPT‑4.1 mini and reducing downstream placement failures from 30.8% to 2.1%.
By Koushikur Islam, Rodrigo N. Calheiros
arXiv:2509. 17446v3 Announce Type: replace-cross Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions.
By Haofeng Huang, Yifei Han, Long Zhang, Bin Li, Yangfan He, Yaxin Xue
arXiv:2608.28926v1 Announce Type: cross
Abstract: We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynami...
By Galo Castillo-L\'opez, Alexis Lombard, Ga\"el de Chalendar, Nasredine Semmar