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See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning.

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Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks., , , and . CoRR, (2018)Unsupervised Dependency Graph Network., , , , , and . ACL (1), page 4767-4784. Association for Computational Linguistics, (2022)Influencing Factors Analysis of People's Answering Behaviours on Social Network Based Questions., , , , , and . UIC/ATC/ScalCom, page 196-203. IEEE Computer Society, (2014)Scattered Mixture-of-Experts Implementation., , , and . CoRR, (2024)SALMON: Self-Alignment with Principle-Following Reward Models., , , , , , , and . CoRR, (2023)Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle., , , , and . NAACL-HLT, page 1660-1672. Association for Computational Linguistics, (2021)Question/Answer Matching for CQA System via Combining Lexical and Sequential Information., , , , and . AAAI, page 275-281. AAAI Press, (2015)Visual Chain-of-Thought Prompting for Knowledge-Based Visual Reasoning., , , , , , and . AAAI, page 1254-1262. AAAI Press, (2024)Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models., , , , , , , and . CoRR, (2024)SALMON: Self-Alignment with Instructable Reward Models., , , , , , , and . ICLR, OpenReview.net, (2024)