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Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain.
An optimum character recognition system using decision functions
C.K. Chow. 1957 · 1957
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt. 1999 · 1999
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On the foundations of noise-free selective classification
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Vqa: Visual question answering
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang. 2020 · 2020
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Thinking like a skeptic: Defeasible inference in natural language
Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, and Yejin Choi. 2020 · 2020
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The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning
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Investigating selective prediction approaches across several tasks in iid, ood, and adversarial settings
Neeraj Varshney, Swaroop Mishra, and Chitta Baral. 2022 · 2022
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Reliable visual question answering: Abstain rather than answer incorrectly
Spencer Whitehead, Suzanne Petryk, Vedaad Shakib, Joseph Gonzalez, Trevor Darrell, Anna Rohrbach, and Marcus Rohrbach. 2022b · 2022
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Obelics: An open web-scale filtered dataset of interleaved image-text documents
Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, and Victor Sanh. 2023 · 2023
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Improved baselines with visual instruction tuning
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Improving automatic vqa evaluation using large language models
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Rephrase, augment, reason: Visual grounding of questions for vision-language models
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Socratic models: Composing zero-shot multimodal reasoning with language
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Reassessing evaluation practices in visual question answering: A case study on out-of-distribution generalization
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Improving selective visual question answering by learning from your peers
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Modular visual question answering via code generation
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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D. Manning. 2023 · 2023
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Post-abstention: Towards reliably re-attempting the abstained instances in qa
Neeraj Varshney and Chitta Baral. 2023 · 2023
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See, say, and segment: Teaching lmms to overcome false premises
Tsung-Han Wu, Giscard Biamby, David Chan, Lisa Dunlap, Ritwik Gupta, Xudong Wang, Joseph E. Gonzalez, and Trevor Darrell. 2023 · 2023
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Mm-react: Prompting chatgpt for multimodal reasoning and action
Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023 · 2023
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Idealgpt: Iteratively decomposing vision and language reasoning via large language models
Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad A Ayyubi, Kai-Wei Chang, and Shih-Fu Chang. 2023 · 2023
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