arXiv AI By Yuhui Wang, Zhixiong Yang, Ming Zhang, Shihan Dou, Zhiheng Xi, Enyu Zhou, Senjie Jin, Yujiong Shen, Dingwei Zhu, Yi Dong, Tao Gui, Qi Zhang, Xuanjing Huang

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

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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
arXiv AI
Sep 4

IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations

IRWOZ 2.0 is a refined dialogue dataset for industrial human‑robot interaction, expanding to 390 dialogues across four domains—Assembly, Delivery, Position, and Relocation. The dataset was improved using large language models (Mistral and Claude‑3.5) for generation and quality refinement, including manual corrections and automated typo removal. Benchmark tests show a substantial boost in dialogue state‑tracking performance, with GPT‑2’s BLEU‑4 score rising from 0.1651 to 0.5604 compared to the original IRWOZ.

By Chen Li, Dimitrios Chrysostomou