Hugging Face Trending Papers

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

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.

arXiv Computation and Language

Sep 25

Proactive for Uncertainty: Cause-Aware Error Diagnosis and Interactive Clarification for Spoken Dialogue Systems

The paper introduces a cause-aware error recovery framework for cascaded Automatic Speech Recognition – Large Language Model (ASR‑LLM) pipelines in Spoken Dialogue Systems. It replaces simple ASR confidence filtering with precision‑focused detectors that use deep ASR latent representations to classify token‑level errors into perception, comprehension, and deletion failures. This fine‑grained diagnosis enables the LLM to execute targeted, multi‑turn clarification strategies, leading to a more than two‑fold increase in recall on domain‑shift errors and significant reductions in word error rate and downstream task errors across varied accents, distortions, and domains.

By Yizhou Peng, Ziyang Ma, Changsong Liu, Yi-Wen Chao, Xie Chen, Eng Siong Chng

llms multimodal

arXiv Computation and Language

Sep 1

Learning to Reason and Use Tools through Unsupervised Fine-Tuning in Task-Oriented Dialog Systems

arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...

By Markel Ferro, Oier Lopez de Lacalle

llms agents fine-tuning safety

arXiv Machine Learning

Jul 31

Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models

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

llms fine-tuning benchmarks

arXiv AI

Aug 10

Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference

arXiv:2608. 06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews.

By Tzu-Cheng Peng (National Taiwan University), Chien Chin Chen (National Taiwan University), Chih-Hao Ku (University of North Texas), Yung-Chun Chang (Taipei Medical University)

llms safety

arXiv AI

Sep 15

Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception

arXiv:2609.15215v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal percept...

By Yanfeng Shi, Yan Song, Junhui Li, Tinggan Huang, Wu Guo, Haoyu Song, Ian McLoughlin

llms benchmarks

arXiv Machine Learning

4d ago

Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs

arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-criti...

By Shenghong Dai, Shiva Kumar Pentyala, Yingchi Liu, Shubham Mehrotra, Suman Banerjee, James Zhu, Bin Bi, Sitaram Asur, Phil Mui

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 2

NILC: Discovering New Intents with LLM-assisted Clustering

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

llms rag benchmarks

arXiv AI

Jul 16

Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models

arXiv:2607. 13408v1 Announce Type: cross Abstract: Recent text-to-audio models generate high-quality audio, but often fail to follow instructions involving multiple sound events and temporal order.

By Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim, Suyoun Kim, Bo-Ru Lu, Qinming Tang, Ankur Gandhe, Hung-yi Lee, Chieh-Chi Kao, Chao Wang

llms benchmarks

arXiv Computation and Language

Sep 4

FrameBench:A Language Understanding Benchmark Based on Frame Semantics

FrameBench is a new benchmark that evaluates language models on their ability to distinguish semantic frames evoked by the same verb in different contexts, using multiple-choice questions grounded in FrameNet-style resources for English and Japanese. The dataset is generated and verified through a pipeline that incorporates native-speaker judgments, and the authors provide both the data and the code for construction and evaluation. Experiments show that small models struggle with this task, while several large models outperform human reference scores.

By Chihiro Yano, Ryohei Sasano

llms benchmarks

arXiv AI

Jul 14

MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

arXiv:2603. 09714v2 Announce Type: replace-cross Abstract: While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored.

By Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo, Ping-Le Tsai, Yen-Ting Piao, Hung-Wei Chen, Ting-Lin Hsiao, Yun-Man Hsu, Ke-Han Lu, Hung-yi Lee

llms benchmarks

arXiv AI

Jun 30

ORCA: Open-ended Response Correctness Assessment for Audio Question Answering

arXiv:2512. 09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art.

By \v{S}imon Sedl\'a\v{c}ek, Sara Barahona, Bolaji Yusuf, Laura Herrera-Alarc\'on, Santosh Kesiraju, Cecilia Bola\~nos, Alicia Lozano-Diez, Sathvik Udupa, Fernando L\'opez, Allison Ferner, Ramani Duraiswami, Jan \v{C}ernock\'y

llms nlp benchmarks

arXiv AI

Jun 24

Selective Capability Unlearning in End-to-End Spoken Language Understanding

arXiv:2606. 24063v1 Announce Type: cross Abstract: Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be removed due to policy or safety constraints.

By Akanksha Singh, Vinod Kumar Kurmi

benchmarks safety

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