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Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages., , , and . CoRR, (2019)Towards Computationally Verifiable Semantic Grounding for Language Models., , , , and . CoRR, (2022)Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning., , , , , , , , , and 23 other author(s). CoRR, (2024)TaTa: A Multilingual Table-to-Text Dataset for African Languages., , , , , , and . CoRR, (2022)The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics., , , , , , , , , and 45 other author(s). CoRR, (2021)Debugging Sequence-to-Sequence Models with Seq2Seq-Vis., , , , , and . BlackboxNLP@EMNLP, page 368-370. Association for Computational Linguistics, (2018)A Framework for Automated Text Generation Benchmarking., , , , , and . SDU@AAAI, volume 3164 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Benchmarking Large Language Model Capabilities for Conditional Generation., , and . ACL (1), page 9194-9213. Association for Computational Linguistics, (2023)LSTM Networks Can Perform Dynamic Counting., , , and . CoRR, (2019)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.