{"list":"other","total":60,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"2107.03374","title":"Evaluating Large Language Models Trained on Code","created":"2021-07-07","cited_by_count":8320},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"2103.03874","title":"Measuring Mathematical Problem Solving With the MATH Dataset","created":"2021-03-05","cited_by_count":5371},{"id":"2205.11916","title":"Large Language Models are Zero-Shot Reasoners","created":"2022-05-24","cited_by_count":4374},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"2304.03442","title":"Generative Agents: Interactive Simulacra of Human Behavior","created":"2023-04-07","cited_by_count":3568},{"id":"2303.12712","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","created":"2023-03-22","cited_by_count":2592},{"id":"2101.00027","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","created":"2020-12-31","cited_by_count":2411},{"id":"2305.16291","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","created":"2023-05-25","cited_by_count":2191},{"id":"2202.03629","title":"Survey of Hallucination in Natural Language Generation","created":"2022-02-08","cited_by_count":2075},{"id":"2203.07814","title":"Competition-Level Code Generation with AlphaCode","created":"2022-02-08","cited_by_count":1462},{"id":"2206.14858","title":"Solving Quantitative Reasoning Problems with Language Models","created":"2022-06-29","cited_by_count":1392},{"id":"2303.17580","title":"HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face","created":"2023-03-30","cited_by_count":1190},{"id":"2205.06175","title":"A Generalist Agent","created":"2022-05-12","cited_by_count":808},{"id":"2309.12284","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","created":"2023-09-21","cited_by_count":735},{"id":"1712.01815","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","created":"2017-12-05","cited_by_count":734},{"id":"2308.09583","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","created":"2023-08-18","cited_by_count":625},{"id":"2306.15626","title":"LeanDojo: Theorem Proving with Retrieval-Augmented Language Models","created":"2023-06-27","cited_by_count":427},{"id":"2109.00110","title":"MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics","created":"2021-08-31","cited_by_count":391},{"id":"2009.03393","title":"Generative Language Modeling for Automated Theorem Proving","created":"2020-09-07","cited_by_count":388},{"id":"2305.17126","title":"Large Language Models as Tool Makers","created":"2023-05-26","cited_by_count":303},{"id":"2210.12283","title":"Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs","created":"2022-10-21","cited_by_count":291},{"id":"1912.01412","title":"Deep Learning for Symbolic Mathematics","created":"2019-12-02","cited_by_count":270},{"id":"2205.12615","title":"Autoformalization with Large Language Models","created":"2022-05-25","cited_by_count":263},{"id":"2207.06881","title":"Recurrent Memory Transformer","created":"2022-07-14","cited_by_count":203},{"id":"2205.11491","title":"HyperTree Proof Search for Neural Theorem Proving","created":"2022-05-23","cited_by_count":197},{"id":"2006.08381","title":"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning","created":"2020-06-15","cited_by_count":161},{"id":"2304.09797","title":"Progressive-Hint Prompting Improves Reasoning in Large Language Models","created":"2023-04-19","cited_by_count":147},{"id":"1910.09336","title":"The Lean mathematical library","created":"2019-10-21","cited_by_count":134},{"id":"2102.06203","title":"Proof Artifact Co-training for Theorem Proving with Language Models","created":"2021-02-11","cited_by_count":132},{"id":"2303.04910","title":"Baldur: Whole-Proof Generation and Repair with Large Language Models","created":"2023-03-08","cited_by_count":119},{"id":"1905.09381","title":"Learning to Prove Theorems via Interacting with Proof Assistants","created":"2019-05-21","cited_by_count":109},{"id":"2205.10893","title":"Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers","created":"2022-05-22","cited_by_count":98},{"id":"2304.11062","title":"Scaling Transformer to 1M tokens and beyond with RMT","created":"2023-04-19","cited_by_count":98},{"id":"1606.04442","title":"DeepMath - Deep Sequence Models for Premise Selection","created":"2016-06-14","cited_by_count":81},{"id":"2303.11568","title":"Large AI Models in Health Informatics: Applications, Challenges, and the Future","created":"2023-03-21","cited_by_count":61},{"id":"2309.04295","title":"FIMO: A Challenge Formal Dataset for Automated Theorem Proving","created":"2023-09-08","cited_by_count":55},{"id":"1805.06502","title":"First Experiments with Neural Translation of Informal to Formal Mathematics","created":"2018-05-10","cited_by_count":53},{"id":"2303.04488","title":"Magnushammer: A Transformer-Based Approach to Premise Selection","created":"2023-03-08","cited_by_count":50},{"id":"1701.06972","title":"Deep Network Guided Proof Search","created":"2017-01-24","cited_by_count":47},{"id":"2007.02924","title":"INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving","created":"2020-07-06","cited_by_count":44},{"id":"2002.07019","title":"Learning to Prove Theorems by Learning to Generate Theorems","created":"2020-02-17","cited_by_count":43},{"id":"2206.00169","title":"Discovering the Hidden Vocabulary of DALLE-2","created":"2022-06-01","cited_by_count":43},{"id":"2305.16366","title":"Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving","created":"2023-05-25","cited_by_count":37},{"id":"1608.02644","title":"Holophrasm: a neural Automated Theorem Prover for higher-order logic","created":"2016-08-08","cited_by_count":29},{"id":"1905.10501","title":"Learning to Reason in Large Theories without Imitation","created":"2019-05-25","cited_by_count":27},{"id":"1804.00595","title":"Learning to Reason with HOL4 tactics","created":"2018-04-02","cited_by_count":14},{"id":"2103.09990","title":"Human-AI Symbiosis: A Survey of Current Approaches","created":"2021-03-18","cited_by_count":8},{"id":"1910.12320","title":"Formalising perfectoid spaces","created":"2019-10-27","cited_by_count":7},{"id":"2104.01052","title":"An Evaluation of the Archive of Formal Proofs","created":"2021-04-02","cited_by_count":4},{"id":"1907.01449","title":"Formalizing the Solution to the Cap Set Problem","created":"2019-07-02","cited_by_count":3},{"id":"2212.11151","title":"Template-Based Conjecturing for Automated Induction in Isabelle/HOL","created":"2022-11-20","cited_by_count":3}],"next_cursor":null}