{"list":"other","total":52,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2201.11903","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","created":"2022-01-28","cited_by_count":12459},{"id":"2110.14168","title":"Training Verifiers to Solve Math Word Problems","created":"2021-10-27","cited_by_count":10109},{"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":"2203.11171","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","created":"2022-03-21","cited_by_count":5708},{"id":"2004.04906","title":"Dense Passage Retrieval for Open-Domain Question Answering","created":"2020-04-10","cited_by_count":3824},{"id":"1904.09751","title":"The Curious Case of Neural Text Degeneration","created":"2019-04-22","cited_by_count":2916},{"id":"1704.05426","title":"A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference","created":"2017-04-18","cited_by_count":2682},{"id":"2205.10625","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","created":"2022-05-21","cited_by_count":1407},{"id":"2002.08155","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","created":"2020-02-19","cited_by_count":1310},{"id":"2112.11446","title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","created":"2021-12-08","cited_by_count":1202},{"id":"1805.04833","title":"Hierarchical Neural Story Generation","created":"2018-05-13","cited_by_count":1193},{"id":"1612.03975","title":"ConceptNet 5.5: An Open Multilingual Graph of General Knowledge","created":"2016-12-12","cited_by_count":1067},{"id":"2107.06499","title":"Deduplicating Training Data Makes Language Models Better","created":"2021-07-14","cited_by_count":714},{"id":"1909.09436","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","created":"2019-09-20","cited_by_count":654},{"id":"2205.09712","title":"Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning","created":"2022-05-19","cited_by_count":334},{"id":"1502.05698","title":"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks","created":"2015-02-19","cited_by_count":314},{"id":"2205.12615","title":"Autoformalization with Large Language Models","created":"2022-05-25","cited_by_count":263},{"id":"1911.00650","title":"Automatic Detection of Generated Text is Easiest when Humans are Fooled","created":"2019-11-02","cited_by_count":261},{"id":"2012.05876","title":"Neurosymbolic AI: The 3rd Wave","created":"2020-12-10","cited_by_count":255},{"id":"2010.05953","title":"COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs","created":"2020-10-12","cited_by_count":254},{"id":"1811.05701","title":"Plan-And-Write: Towards Better Automatic Storytelling","created":"2018-11-14","cited_by_count":234},{"id":"2210.13312","title":"Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs","created":"2022-10-24","cited_by_count":220},{"id":"2210.06774","title":"Re3: Generating Longer Stories With Recursive Reprompting and Revision","created":"2022-10-13","cited_by_count":193},{"id":"2210.07128","title":"Language Models of Code are Few-Shot Commonsense Learners","created":"2022-10-13","cited_by_count":160},{"id":"2001.05139","title":"A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation","created":"2020-01-15","cited_by_count":116},{"id":"2004.14967","title":"PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking","created":"2020-04-30","cited_by_count":105},{"id":"1909.10705","title":"Do Massively Pretrained Language Models Make Better Storytellers?","created":"2019-09-24","cited_by_count":90},{"id":"1909.04076","title":"Counterfactual Story Reasoning and Generation","created":"2019-09-09","cited_by_count":88},{"id":"2009.07758","title":"GLUCOSE: GeneraLized and COntextualized Story Explanations","created":"2020-09-16","cited_by_count":86},{"id":"2107.02794","title":"Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning","created":"2021-07-06","cited_by_count":85},{"id":"2010.03150","title":"PyMT5: multi-mode translation of natural language and Python code with transformers","created":"2020-10-07","cited_by_count":80},{"id":"1706.01331","title":"Event Representations for Automated Story Generation with Deep Neural Nets","created":"2017-06-05","cited_by_count":63},{"id":"1809.10736","title":"Controllable Neural Story Plot Generation via Reward Shaping","created":"2018-09-27","cited_by_count":49},{"id":"2109.05438","title":"\"Let Your Characters Tell Their Story\": A Dataset for Character-Centric Narrative Understanding","created":"2021-09-12","cited_by_count":48},{"id":"1904.02357","title":"Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation","created":"2019-04-04","cited_by_count":43},{"id":"1909.03480","title":"Story Realization: Expanding Plot Events into Sentences","created":"2019-09-08","cited_by_count":35},{"id":"2210.07109","title":"Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence","created":"2022-10-13","cited_by_count":35},{"id":"2104.08350","title":"ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning","created":"2021-04-16","cited_by_count":30},{"id":"2105.01311","title":"Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning","created":"2021-05-04","cited_by_count":29},{"id":"2108.12960","title":"LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation","created":"2021-08-30","cited_by_count":24},{"id":"2210.08459","title":"StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning","created":"2022-10-16","cited_by_count":21},{"id":"2010.07074","title":"Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries","created":"2020-10-14","cited_by_count":19},{"id":"2104.07064","title":"Is Everything in Order? A Simple Way to Order Sentences","created":"2021-04-14","cited_by_count":15},{"id":"2109.10475","title":"Salience-Aware Event Chain Modeling for Narrative Understanding","created":"2021-09-22","cited_by_count":15},{"id":"2112.08593","title":"Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning","created":"2021-12-16","cited_by_count":14},{"id":"2105.15054","title":"Telling Stories through Multi-User Dialogue by Modeling Character Relations","created":"2021-05-31","cited_by_count":13},{"id":"2109.03754","title":"Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories","created":"2021-09-08","cited_by_count":12},{"id":"2211.00676","title":"Towards Inter-character Relationship-driven Story Generation","created":"2022-11-01","cited_by_count":11},{"id":"1904.11942","title":"Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding","created":"2019-04-26","cited_by_count":5},{"id":"2202.07880","title":"$\\rm{C {\\small IS}}^2$: A Simplified Commonsense Inference Evaluation for Story Prose","created":"2022-02-16","cited_by_count":4}],"next_cursor":null}