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0], ["doi:10.1109/tetc.2023.3322033", "Enhancing Neural Architecture Search With Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices", 2023, 0], ["doi:10.1109/tevc.2023.3348475", "Toward Evolutionary Multitask Convolutional Neural Architecture Search", 2023, 0], ["doi:10.1109/tits.2023.3259322", "A Survey on Safety-Critical Driving Scenario Generation\u2014A Methodological Perspective", 2023, 0], ["doi:10.1109/tnnls.2023.3250269", "A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems", 2023, 0], ["doi:10.1109/tpami.2023.3240565", "DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography", 2023, 0], ["doi:10.1109/tpami.2023.3292075", "Transfer Learning in Deep Reinforcement Learning: A Survey", 2023, 0], ["doi:10.1109/tpami.2023.3294394", "Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning", 2023, 0], ["doi:10.1109/tro.2023.3257541", "Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot 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Innovation in computer systems continues at a high pace", 2023, 0], ["doi:10.1787/6f256d17-en", "Eroom\u2019s Law and the decline in the productivity of biopharmaceutical R&D", 2023, 0], ["doi:10.1787/7b7b1bce-en", "Artificial intelligence for science and engineering: A priority for public investment in research and development", 2023, 0], ["doi:10.1787/b885eecd-en", "Lessons from shortcomings in machine learning for medical imaging", 2023, 0], ["doi:10.1787/f1262928-en", "Preface", 2023, 0], ["doi:10.18653/v1/2023.acl-long", "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", 2023, 0], ["doi:10.18653/v1/2023.acl-long.147", "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models", 2023, 0], ["doi:10.18653/v1/2023.acl-long.267", "Pre-Training to Learn in Context", 2023, 0], ["doi:10.18653/v1/2023.acl-long.294", "Reasoning with Language Model Prompting: A Survey", 2023, 0], ["doi:10.18653/v1/2023.acl-long.385", "Few-shot In-context Learning on Knowledge Base Question Answering", 2023, 0], ["doi:10.18653/v1/2023.acl-long.557", "Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions", 2023, 0], ["doi:10.18653/v1/2023.acl-long.626", "Is GPT-3 a Good Data Annotator?", 2023, 0], ["doi:10.18653/v1/2023.acl-long.727", "Exploring and Verbalizing Academic Ideas by Concept Co-occurrence", 2023, 0], ["doi:10.18653/v1/2023.acl-long.767", "Training Trajectories of Language Models Across Scales", 2023, 0], ["doi:10.18653/v1/2023.acl-long.870", "Can Large Language Models Be an Alternative to Human Evaluations?", 2023, 0], ["doi:10.18653/v1/2023.acl-short", "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", 2023, 0], ["doi:10.18653/v1/2023.acl-short.38", "Grokking of Hierarchical Structure in Vanilla Transformers", 2023, 0], ["doi:10.18653/v1/2023.conll-babylm.1", "Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora", 2023, 0], ["doi:10.18653/v1/2023.emnlp-demo.49", "Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.153", "G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.313", "LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.322", "Query Rewriting in Retrieval-Augmented Large Language Models", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.330", "Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.398", "Enabling Large Language Models to Generate Text with Citations", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.495", "Active Retrieval Augmented Generation", 2023, 0], ["doi:10.18653/v1/2023.emnlp-main.557", "SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models", 2023, 0], ["doi:10.18653/v1/2023.findings-acl.247", "Why Can GPT Learn In-Context? 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0], ["arxiv:2201.02177", "Grokking: Generalization beyond overfitting on small algorithmic datasets", 2022, 0], ["arxiv:2202.07646", "Quantifying Memorization Across Neural Language Models", 2022, 0], ["arxiv:2203.02155", "Training language models to follow instructions with human feedback", 2022, 0], ["arxiv:2203.11171", "Self-consistency improves chain of thought reasoning in language models", 2022, 0], ["arxiv:2206.08896", "Evolution through large models, 2022", 2022, 0], ["arxiv:2209.11895", "In-context learning and induction heads", 2022, 0], ["arxiv:2210.05359", "Mind\u2019s eye: Grounded language model reasoning through simulation", 2022, 0], ["arxiv:2211.09085", "Galactica: A Large Language Model for Science", 2022, 0], ["arxiv:2211.09760", "Velo: Training versatile learned optimizers by scaling up", 2022, 0], ["arxiv:2212.08073", "Constitutional AI: Harmlessness from AI Feedback", 2022, 0], ["doi:10.1007/978-3-030-99736-6_34", "Goldilocks: Just-Right Tuning of BERT for 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