SFL-MTSC improves slot F1 and overall accuracy in zero-shot multi-intent SLU on MAC-SLU by decomposing predictions into intent-specific frames, applying domain-intent grouping and slot clustering, and retaining reliable frames via path support scoring.
A preliminary evaluation of chatgpt for zero-shot dialogue understanding,
3 Pith papers cite this work, alongside 21 external citations. Polarity classification is still indexing.
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A masked-token hit-rate comparison method detects pretraining data membership in black-box LLMs with performance comparable to white-box approaches.
VLK-RL verifies LLM-derived constraints and maps them into structured state representations to improve RL performance on long-horizon cross-domain dialogue tasks.
citing papers explorer
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SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
SFL-MTSC improves slot F1 and overall accuracy in zero-shot multi-intent SLU on MAC-SLU by decomposing predictions into intent-specific frames, applying domain-intent grouping and slot clustering, and retaining reliable frames via path support scoring.
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MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
A masked-token hit-rate comparison method detects pretraining data membership in black-box LLMs with performance comparable to white-box approaches.
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Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue
VLK-RL verifies LLM-derived constraints and maps them into structured state representations to improve RL performance on long-horizon cross-domain dialogue tasks.