Search Results - Swayamdipta, Swabha

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  1. 1
    Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization

    Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization by Nam, Yoonsoo, Lehavi, Adam, Yang, Daniel, Bose, Digbalay, Swayamdipta, Swabha, Narayanan, Shrikanth

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  2. 2
    Annotating FrameNet via Structure-Conditioned Language Generation

    Annotating FrameNet via Structure-Conditioned Language Generation by Cui, Xinyue, Swayamdipta, Swabha

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  3. 3
    Generating Function Names to Improve Comprehension of Synthesized Programs

    Generating Function Names to Improve Comprehension of Synthesized Programs by Nazari, Amirmohammad, Swayamdipta, Swabha, Chattopadhyay, Souti, Raghothaman, Mukund

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  4. 4
    Nom Nom: Explanatory Function Names for Program Synthesizers

    Nom Nom: Explanatory Function Names for Program Synthesizers by Nazari, Amirmohammad, Chattopadhyay, Souti, Swayamdipta, Swabha, Raghothaman, Mukund

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  5. 5
    Logits of API-Protected LLMs Leak Proprietary Information

    Logits of API-Protected LLMs Leak Proprietary Information by Finlayson, Matthew, Ren, Xiang, Swayamdipta, Swabha

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  6. 6
    Compare without Despair: Reliable Preference Evaluation with Generation Separability

    Compare without Despair: Reliable Preference Evaluation with Generation Separability by Ghosh, Sayan, Srinivasan, Tejas, Swayamdipta, Swabha

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  7. 7
    Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?

    Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks? by Khurana, Urja, Nalisnick, Eric, Fokkens, Antske, Swayamdipta, Swabha

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  8. 8
    Generative Explanations for Program Synthesizers

    Generative Explanations for Program Synthesizers by Nazari, Amirmohammad, Chattopadhyay, Souti, Swayamdipta, Swabha, Raghothaman, Mukund

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  9. 9
    Understanding Dataset Difficulty with \(\mathcal{V}\)-Usable Information

    Understanding Dataset Difficulty with \(\mathcal{V}\)-Usable Information by Kawin Ethayarajh, Choi, Yejin, Swayamdipta, Swabha

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  10. 10
    Closing the Curious Case of Neural Text Degeneration

    Closing the Curious Case of Neural Text Degeneration by Finlayson, Matthew, Hewitt, John, Koller, Alexander, Swayamdipta, Swabha, Sabharwal, Ashish

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  11. 11
    WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

    WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation by Liu, Alisa, Swayamdipta, Swabha, Smith, Noah A, Choi, Yejin

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  12. 12
    Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression

    Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression by Gulati, Aryan, Dong, Xingjian, Hurtado, Carlos, Shekkizhar, Sarath, Swayamdipta, Swabha, Ortega, Antonio

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  13. 13
    Investigating the Benefits of Free-Form Rationales

    Investigating the Benefits of Free-Form Rationales by Jiao, Sun, Swayamdipta, Swabha, May, Jonathan, Ma, Xuezhe

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  14. 14
    NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation

    NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation by Howard, Phillip, Singer, Gadi, Lal, Vasudev, Choi, Yejin, Swayamdipta, Swabha

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  15. 15
    NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge

    NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge by Howard, Phillip, Wang, Junlin, Lal, Vasudev, Singer, Gadi, Choi, Yejin, Swayamdipta, Swabha

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  16. 16
    Reframing Human-AI Collaboration for Generating Free-Text Explanations

    Reframing Human-AI Collaboration for Generating Free-Text Explanations by Wiegreffe, Sarah, Hessel, Jack, Swayamdipta, Swabha, Riedl, Mark, Choi, Yejin

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  17. 17
    OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants

    OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants by Jaspreet Ranjit, Joshi, Brihi, Dorn, Rebecca, Petry, Laura, Koumoundouros, Olga, Bottarini, Jayne, Liu, Peichen, Rice, Eric, Swayamdipta, Swabha

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  18. 18
    Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization

    Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization by Nam, Yoonsoo, Lehavi, Adam, Yang, Daniel, Bose, Digbalay, Swayamdipta, Swabha, Narayanan, Shrikanth

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  19. 19
    Sister Help: Data Augmentation for Frame-Semantic Role Labeling

    Sister Help: Data Augmentation for Frame-Semantic Role Labeling by Pancholy, Ayush, Petruck, Miriam R L, Swayamdipta, Swabha

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  20. 20
    COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements

    COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements by Zhou, Xuhui, Zhu, Hao, Yerukola, Akhila, Davidson, Thomas, Hwang, Jena D, Swayamdipta, Swabha, Sap, Maarten

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