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Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection.

, , , , , , , , and . LREC/COLING, page 2354-2364. ELRA and ICCL, (2024)

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DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning., , , , , , , , , and . ACL (1), page 4706-4721. Association for Computational Linguistics, (2024)BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses., , , , and . LREC/COLING, page 2458-2467. ELRA and ICCL, (2024)Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection., , , , , , , , and . LREC/COLING, page 2354-2364. ELRA and ICCL, (2024)CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery., , , , , , , , , and 6 other author(s). CoRR, (2024)DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations., , , , , and . NAACL-HLT (Findings), page 800-813. Association for Computational Linguistics, (2024)FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue., , , , , , and . ACL (1), page 6532-6546. Association for Computational Linguistics, (2023)How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data., , , , , , , , , and 4 other author(s). CoRR, (2024)