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Competition-Level Problems are Effective LLM Evaluators., , , , , , , , , and 1 other author(s). CoRR, (2023)S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models., , , , , and . CoRR, (2023)MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models., , , , , , , and . EMNLP (Findings), page 1434-1447. Association for Computational Linguistics, (2023)Answer-Based Entity Extraction and Alignment for Visual Text Question Answering., , , , , , , , and . ACM Multimedia, page 9487-9491. ACM, (2023)S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering., , , , , , and . ACL (2), page 1731-1740. Association for Computational Linguistics, (2023)OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments., , , , , , , , , and 7 other author(s). CoRR, (2024)TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering., , , , , , , , , and 1 other author(s). CoRR, (2023)Assessing Knowledge Editing in Language Models via Relation Perspective., , , , , , and . CoRR, (2023)Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder., , , , and . COLING, page 1379-1390. International Committee on Computational Linguistics, (2022)Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question., , , , and . CoRR, (2023)