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.
The paper introduces a pipeline that generates intent‑labeled, two‑channel conversational speech from relational event lists, enabling controlled synthesis of full‑duplex dialogue with 42 phenomena across eight families in English and Mandarin. By having an LLM author each event’s speaker, text, conversational act, and attachment, and then aligning and timing these events independently, the system produces diverse, realistic turn‑taking signals. Experiments show that models trained on this synthetic corpus achieve higher floor‑occupancy accuracy and better start‑speaking/listening F1 scores compared to models trained on prior data.
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.
Hy‑MultiTurn is a Chinese benchmark designed to evaluate deep multi‑turn dialogue understanding over long interactions. It introduces six controlled evaluation modes—constraint memory, precise execution, constraint synthesis, object localization, action suppression, and reference resolution—across 209 tasks ranging from 12 to 76 turns, incorporating dialogue length, irrelevant distractions, and colloquial phrasing. Testing 22 state‑of‑the‑art models shows the benchmark is highly challenging, with even the best model meeting all criteria only 41.1% of the time and no model excelling in every mode.
The paper introduces Omni Demand Understanding (ODU), a benchmark designed to test whether multimodal models can infer a user's underlying demand from complex audio‑visual interactions. ODU requires models to detect the presence of a demand and infer intent using multimodal and conversational context, evaluated across single‑turn and multi‑turn scenarios. The authors built ODU‑Bench through a taxonomy‑guided approach, agentic video generation, and human‑recorded interactions, and found that even top models like Gemini 3.1 Pro recover only 44.7% of key information, with many models exhibiting high false‑trigger rates.
arXiv:2609.22200v1 Announce Type: new Abstract: LLM assistants and agentic systems log long multi-turn conversations. AI providers often scan these conversations for Personally Identifiable Informati...
SimXAI is a lightweight, LLM‑centric conversational layer that replaces traditional intent recognition, dialogue management, and template‑based response modules in GUS‑style ConvXAI agents. By using a single tool‑calling LLM to interface with existing XAI resources, it provides a modular interface that improves function‑selection accuracy, faithfulness, and handling of context‑dependent requests. In a user study, substituting SimXAI for the conversational layer of the TalkToModel agent led to more accurate task completion in fewer turns, demonstrating enhanced conversational quality while preserving faithfulness.