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.
arXiv:2607. 07974v1 Announce Type: cross Abstract: Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems.
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.
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."
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...
The paper introduces Aslema, a system for the NADI 2026 Shared Task 5, which includes intent recognition and slot filling. The authors evaluate four omni LLMs in zero‑shot and fine‑tuned settings, finding that fine‑tuning consistently outperforms zero‑shot inference. They further augment data by generating culturally grounded Tunisian Derja utterances with an LLM and synthetic speech via voice cloning, which improves performance; the final system based on Qwen3‑Omni‑30B achieves 86.8% intent accuracy and 34.7 WER on devtest, ranking 1st in slot filling and 4th in intent recognition on the official test set.
Aslema is a system developed for the NADI 2026 Shared Task 5, which involves intent recognition and slot filling. The team evaluated four omni LLMs in a zero‑shot setting and found that fine‑tuned models consistently outperform zero‑shot inference. They further improved performance by augmenting data with culturally grounded Tunisian Derja utterances generated by an LLM and synthetic speech produced via voice cloning, achieving top‑ranked results on the official test set.
arXiv:2607. 22635v1 Announce Type: new Abstract: Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations.
The paper introduces a benchmark for topic matching in real-world ASR transcripts from contact centers, where noisy, punctuation‑free speech data must be classified into predefined topics. It presents a human‑annotated dataset of topic‑utterance judgments and evaluates three matcher types—regex, zero‑shot sentence embeddings, and Gemini‑based LLMs—using two topic representations: keyphrases and natural language descriptions. Experiments show that lightweight LLM matchers outperform the other methods, especially when natural language descriptions are used.
arXiv:2607. 16021v1 Announce Type: cross Abstract: Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems.
The paper introduces LOGIC (Logit‑Space Integration for Contextual Biasing), a new framework that injects contextual entity information directly into the decoding layer of Speech Large Language Models, bypassing the limitations of prompt‑based methods. LOGIC operates with constant‑time complexity regardless of the size of the entity list, and experiments with the Phi‑4‑MM model across 11 multilingual locales show an average 9% relative reduction in Entity WER while adding only a 0.30% increase in False Alarm Rate.
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.
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.