Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework | Paper Citation Record

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This paper cites Language models are few-shot learners.Advances in neural information processing sys- tems, 33:1877–1901,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Language models are few-shot learners.Advances in neural information processing sys- tems, 33:1877–1901,

Reference 1

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework A Simple Framework for Contrastive Learning of Visual Representations

Reference 3

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This paper cites Debiased contrastive learning.Advances in neural infor- mation processing systems, 33:8765–8775,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Debiased contrastive learning.Advances in neural infor- mation processing systems, 33:8765–8775,

Reference 4

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This paper cites Maximum likelihood from incom- plete data via the em algorithm.Journal of the Royal Sta- tistical Society: Series B (Methodological), 39(1):1–22,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Maximum likelihood from incom- plete data via the em algorithm.Journal of the Royal Sta- tistical Society: Series B (Methodological), 39(1):1–22,

Reference 5

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This paper cites [Kaya and Bilge, 2019] Mahmut Kaya and Hasan S ¸akir Bilge.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework [Kaya and Bilge, 2019] Mahmut Kaya and Hasan S ¸akir Bilge

Reference 13

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This paper cites On information and sufficiency.The Annals of Mathematical Statistics, 22(1):79–86,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework On information and sufficiency.The Annals of Mathematical Statistics, 22(1):79–86,

Reference 14

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Observation d05f3c53-96ab-4c3d-8a55-b5bdbdc99ee6 · outbound

This paper cites An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

Reference 16

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This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 17

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This paper cites Language- emphasized cross-lingual in-context learning for multilin- gual llm.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Language- emphasized cross-lingual in-context learning for multilin- gual llm

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.592519Z

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This paper cites Least squares quantization in pcm.IEEE Transactions on Information Theory, 28(2):129–137,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Least squares quantization in pcm.IEEE Transactions on Information Theory, 28(2):129–137,

Reference 22

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This paper cites Generalized in- tent discovery: Learning from open world dialogue sys- tem.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Generalized in- tent discovery: Learning from open world dialogue sys- tem

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.552192Z

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This paper cites Decoupling pseudo label disambiguation and representation learning for generalized intent discovery.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Decoupling pseudo label disambiguation and representation learning for generalized intent discovery

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.541297Z

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This paper cites [Shannon, 1948] Claude E Shannon.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework [Shannon, 1948] Claude E Shannon

Reference 26

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This paper cites Concerning nonnegative matrices and doubly stochastic matrices.Pacific Journal of Mathematics, 21(2):343–348,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Concerning nonnegative matrices and doubly stochastic matrices.Pacific Journal of Mathematics, 21(2):343–348,

Reference 28

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Observation 21855c5e-8c8c-4a81-895c-9ba86a4a387e · outbound

This paper cites Towards Open Intent Discovery for Conversational Text.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Towards Open Intent Discovery for Conversational Text

Reference 30

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Observation 97c9a29b-c0b9-482c-831b-c3c030446961 · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization.IEEE Transactions on Knowledge and Data Engineering,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Generalizing to unseen domains: A survey on domain generalization.IEEE Transactions on Knowledge and Data Engineering,

Reference 31

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This paper cites an unresolved cited work.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Unresolved cited work

Reference 32

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This paper cites Elevating knowledge-enhanced entity and relation- ship understanding for sarcasm detection.IEEE Transac- tions on Knowledge and Data Engineering, 37(6):3356– 3371,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Elevating knowledge-enhanced entity and relation- ship understanding for sarcasm detection.IEEE Transac- tions on Knowledge and Data Engineering, 37(6):3356– 3371,

Reference 33

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This paper cites Hierarchical Tagger with Multi-task Learning for Cross-domain Slot Filling.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Hierarchical Tagger with Multi-task Learning for Cross-domain Slot Filling

Reference 34

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Observation 1f3d81be-3dd4-49b3-94fd-0a4297512b0c · outbound

This paper cites A prompt- based hierarchical pipeline for cross-domain slot filling.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework A prompt- based hierarchical pipeline for cross-domain slot filling

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This paper cites $\mathbf{{}^{12}{C} + {}^{12}{C}}$ Fusion $\boldsymbol{S^*}$-factor from a Full-microscopic Nuclear Model.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework $\mathbf{{}^{12}{C} + {}^{12}{C}}$ Fusion $\boldsymbol{S^*}$-factor from a Full-microscopic Nuclear Model

Reference 37

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This paper cites Generalized out-of-distribution detec- tion: A survey.International Journal of Computer Vision, 132(12):5635–5662,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Generalized out-of-distribution detec- tion: A survey.International Journal of Computer Vision, 132(12):5635–5662,

Reference 38

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This paper cites Discovering new intents with deep aligned clustering.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Discovering new intents with deep aligned clustering

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This paper cites Out-of-domain detection for natural language un- derstanding in dialog systems.IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:1198–1209,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Out-of-domain detection for natural language un- derstanding in dialog systems.IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:1198–1209,

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This paper cites Knn-contrastive learning for out-of- distribution intent classification.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Knn-contrastive learning for out-of- distribution intent classification

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Observation 35a5a80d-1949-4f25-8d0d-a02c0fec91dc · outbound

This paper cites Knn-contrastive learning for out-of-domain intent discovery.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Knn-contrastive learning for out-of-domain intent discovery

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This paper cites A prompt learning framework with large language model augmentation for few-shot multi-label in- tent detection.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework A prompt learning framework with large language model augmentation for few-shot multi-label in- tent detection

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This paper cites Linguistically-enriched and context- awarezero-shot slot filling.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Linguistically-enriched and context- awarezero-shot slot filling

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This paper cites A Survey on Out-of-Distribution Detection in NLP.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework A Survey on Out-of-Distribution Detection in NLP

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This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Fixmatch: Simplifying semi-supervised learning with consistency and confidence

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understand- ing.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Bert: Pre-training of deep bidirectional transformers for language understand- ing

Reference 1977

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.703455Z

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Observation 0f2db7e0-5b6b-49bf-9a7b-b8880b9a4742 · outbound

This paper cites Decoupled Weight Decay Regularization.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Decoupled Weight Decay Regularization

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 55(9):1–35,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.ACM Computing Surveys, 55(9):1–35,

Reference 1995

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This paper cites Improving zero- shot cross-domain slot filling via transformer-based slot semantics fusion.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Improving zero- shot cross-domain slot filling via transformer-based slot semantics fusion

Reference 2013

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This paper cites Accurate, large minibatch sgd: Training ima- genet in 1 hour.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Accurate, large minibatch sgd: Training ima- genet in 1 hour

Reference 2016

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This paper cites Ptr: Prompt tuning with rules for text classification.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Ptr: Prompt tuning with rules for text classification

Reference 2017

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Observation c71855d1-bf98-4afc-9021-d743649ea1d8 · outbound

This paper cites Openprompt: An open-source framework for prompt-learning.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Openprompt: An open-source framework for prompt-learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.693756Z

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Observation 2e9f19cd-5641-4a79-816b-2d71b90504f5 · outbound

This paper cites Efficient Intent Detection with Dual Sentence Encoders.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Efficient Intent Detection with Dual Sentence Encoders

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This paper cites MIT Press, Cam- bridge, MA,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework MIT Press, Cam- bridge, MA,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.662104Z

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Observation 3b48529a-9c32-4d5c-bd34-960fba5e353e · outbound

This paper cites Revisiting consistency regularization for semi-supervised learning.International Journal of Com- puter Vision, 131(3):626–643,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Revisiting consistency regularization for semi-supervised learning.International Journal of Com- puter Vision, 131(3):626–643,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.683000Z

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Observation eab22f25-e18e-4c93-a011-396e983f1a78 · outbound

This paper cites A brief review of domain adaptation.Advances in data science and infor- mation engineering: proceedings from ICDATA 2020 and IKE 2020, pages 877–894,.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework A brief review of domain adaptation.Advances in data science and infor- mation engineering: proceedings from ICDATA 2020 and IKE 2020, pages 877–894,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.672359Z

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Observation fc52ee15-1808-4dfe-9ec1-4f5f04ca1d0f · outbound

This paper cites Distance-based out-of-distribution detection with confidence scores.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework Distance-based out-of-distribution detection with confidence scores

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:16:23.440949Z

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Observation 232a3c34-13f9-4a78-9254-f0515d61c0d6 · outbound

This paper cites [Little, 1995] Roderick JA Little.

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework [Little, 1995] Roderick JA Little

Reference 2025

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