AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
arXiv preprint arXiv:2208.14536 , year=
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A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
citing papers explorer
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AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
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From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.