JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
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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.
arXiv:2606. 17904v1 Announce Type: new Abstract: Language models increasingly serve as advisory systems in maintenance operations.
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.
arXiv:2609.35812v1 Announce Type: new Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. E...
arXiv:2608.27750v1 Announce Type: new Abstract: The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to ex...
Evaluation of Computer-Use Agents (CUAs) is often limited to the final deliverables they create (at the end of hundreds of steps) and assessed with functional verifiers, as seen in OSWorld. However, s...