{"list":"other","total":81,"rows":[{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1706.03741","title":"Deep reinforcement learning from human preferences","created":"2017-06-12","cited_by_count":3912},{"id":"2308.11432","title":"A Survey on Large Language Model based Autonomous Agents","created":"2023-08-22","cited_by_count":2242},{"id":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"1612.06890","title":"CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning","created":"2016-12-20","cited_by_count":1457},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1802.01933","title":"A Survey Of Methods For Explaining Black Box Models","created":"2018-02-06","cited_by_count":973},{"id":"1705.08500","title":"Selective Classification for Deep Neural Networks","created":"2017-05-23","cited_by_count":852},{"id":"2301.00234","title":"A Survey on In-context Learning","created":"2022-12-31","cited_by_count":721},{"id":"1910.12656","title":"Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration","created":"2019-10-28","cited_by_count":364},{"id":"1901.09192","title":"SelectiveNet: A Deep Neural Network with an Integrated Reject Option","created":"2019-01-26","cited_by_count":350},{"id":"2210.01240","title":"Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought","created":"2022-10-03","cited_by_count":337},{"id":"1703.03717","title":"Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations","created":"2017-03-10","cited_by_count":294},{"id":"2006.01862","title":"Consistent Estimators for Learning to Defer to an Expert","created":"2020-06-02","cited_by_count":271},{"id":"2002.05867","title":"Transformers as Soft Reasoners over Language","created":"2020-02-14","cited_by_count":270},{"id":"2010.05953","title":"COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs","created":"2020-10-12","cited_by_count":254},{"id":"1711.06664","title":"Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer","created":"2017-11-17","cited_by_count":233},{"id":"2309.15402","title":"Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future","created":"2023-09-27","cited_by_count":184},{"id":"1606.05433","title":"FVQA: Fact-based Visual Question Answering","created":"2016-06-17","cited_by_count":181},{"id":"2106.10328","title":"Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets","created":"2021-06-18","cited_by_count":181},{"id":"1906.09208","title":"Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices","created":"2019-06-21","cited_by_count":174},{"id":"1902.06977","title":"Evaluating model calibration in classification","created":"2019-02-19","cited_by_count":168},{"id":"2011.01821","title":"Minimax Pareto Fairness: A Multi Objective Perspective","created":"2020-11-03","cited_by_count":153},{"id":"2104.08661","title":"Explaining Answers with Entailment Trees","created":"2021-04-17","cited_by_count":152},{"id":"2005.00582","title":"Learning to Complement Humans","created":"2020-05-01","cited_by_count":124},{"id":"2010.04389","title":"A Survey of Knowledge-Enhanced Text Generation","created":"2020-10-09","cited_by_count":120},{"id":"2102.09692","title":"To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making","created":"2021-02-19","cited_by_count":118},{"id":"2107.11277","title":"Machine Learning with a Reject Option: A survey","created":"2021-07-23","cited_by_count":109},{"id":"1903.12220","title":"The Algorithmic Automation Problem: Prediction, Triage, and Human Effort","created":"2019-03-28","cited_by_count":101},{"id":"1506.06112","title":"The Extreme Value Machine","created":"2015-06-19","cited_by_count":78},{"id":"2103.08902","title":"Differentiable Learning Under Triage","created":"2021-03-16","cited_by_count":76},{"id":"2112.10327","title":"Classifier Calibration: A survey on how to assess and improve predicted class probabilities","created":"2021-12-20","cited_by_count":73},{"id":"2102.13004","title":"Towards Unbiased and Accurate Deferral to Multiple Experts","created":"2021-02-25","cited_by_count":72},{"id":"1907.00208","title":"Deep Gamblers: Learning to Abstain with Portfolio Theory","created":"2019-06-29","cited_by_count":71},{"id":"2301.06197","title":"Who Should Predict? Exact Algorithms For Learning to Defer to Humans","created":"2023-01-15","cited_by_count":70},{"id":"2202.03673","title":"Calibrated Learning to Defer with One-vs-All Classifiers","created":"2022-02-08","cited_by_count":69},{"id":"1807.01771","title":"Direct Uncertainty Prediction for Medical Second Opinions","created":"2018-07-04","cited_by_count":61},{"id":"2210.16955","title":"Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles","created":"2022-10-30","cited_by_count":60},{"id":"2307.08674","title":"TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT","created":"2023-07-17","cited_by_count":59},{"id":"2109.14723","title":"BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief","created":"2021-09-29","cited_by_count":57},{"id":"2207.09584","title":"Sample Efficient Learning of Predictors that Complement Humans","created":"2022-07-19","cited_by_count":46},{"id":"2006.11845","title":"Classification Under Human Assistance","created":"2020-06-21","cited_by_count":42},{"id":"2001.09604","title":"Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences","created":"2020-01-27","cited_by_count":41},{"id":"1909.02963","title":"Regression Under Human Assistance","created":"2019-09-06","cited_by_count":40},{"id":"2109.14591","title":"Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration","created":"2021-09-29","cited_by_count":40},{"id":"2206.07948","title":"Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts","created":"2022-06-16","cited_by_count":40},{"id":"2310.14774","title":"Principled Approaches for Learning to Defer with Multiple Experts","created":"2023-10-23","cited_by_count":38},{"id":"2204.13074","title":"Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement","created":"2022-04-27","cited_by_count":37},{"id":"2010.14134","title":"Selective Classification Can Magnify Disparities Across Groups","created":"2020-10-27","cited_by_count":36},{"id":"2105.10614","title":"Human-AI Collaboration with Bandit Feedback","created":"2021-05-22","cited_by_count":36},{"id":"1802.09548","title":"Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction","created":"2018-02-26","cited_by_count":35},{"id":"2211.03318","title":"Fixing Model Bugs with Natural Language Patches","created":"2022-11-07","cited_by_count":33},{"id":"2303.09224","title":"Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction","created":"2023-03-16","cited_by_count":32},{"id":"2305.05976","title":"Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge","created":"2023-05-10","cited_by_count":32},{"id":"2104.08825","title":"Flexible Generation of Natural Language Deductions","created":"2021-04-18","cited_by_count":28},{"id":"2306.13723","title":"Human-AI Coevolution","created":"2023-06-23","cited_by_count":27},{"id":"2311.01106","title":"In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer","created":"2023-11-02","cited_by_count":26},{"id":"2109.00893","title":"A Survey on Open Set Recognition","created":"2021-08-18","cited_by_count":25},{"id":"2206.15465","title":"Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values","created":"2022-06-30","cited_by_count":25},{"id":"2304.07306","title":"Learning to Defer with Limited Expert Predictions","created":"2023-04-14","cited_by_count":25},{"id":"2103.02381","title":"Human-AI Interactions in Public Sector Decision-Making: \"Automation Bias\" and \"Selective Adherence\" to Algorithmic Advice","created":"2021-03-03","cited_by_count":22},{"id":"1808.09123","title":"Investigating Human + Machine Complementarity for Recidivism Predictions","created":"2018-08-28","cited_by_count":19},{"id":"2101.05360","title":"Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible","created":"2021-01-13","cited_by_count":19},{"id":"2012.02898","title":"Learning Interpretable Concept-Based Models with Human Feedback","created":"2020-12-04","cited_by_count":17},{"id":"2108.07392","title":"Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis","created":"2021-08-17","cited_by_count":17},{"id":"1307.0426","title":"An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation","created":"2013-07-01","cited_by_count":16},{"id":"2009.09723","title":"Machine Guides, Human Supervises: Interactive Learning with Global Explanations","created":"2020-09-21","cited_by_count":16},{"id":"2206.13202","title":"Human-AI Collaboration in Decision-Making: Beyond Learning to Defer","created":"2022-06-27","cited_by_count":16},{"id":"2307.09254","title":"Selective Generation for Controllable Language Models","created":"2023-07-18","cited_by_count":15},{"id":"2005.00808","title":"Dimensions of Diversity in Human Perceptions of Algorithmic Fairness","created":"2020-05-02","cited_by_count":13},{"id":"2112.10768","title":"Improving Learning-to-Defer Algorithms Through Fine-Tuning","created":"2021-12-18","cited_by_count":11},{"id":"2204.09347","title":"Active Few-Shot Learning with FASL","created":"2022-04-20","cited_by_count":11},{"id":"2303.09297","title":"Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals","created":"2023-03-16","cited_by_count":9},{"id":"2110.01552","title":"Perhaps PTLMs Should Go to School -- A Task to Assess Open Book and Closed Book QA","created":"2021-10-04","cited_by_count":8},{"id":"1812.03219","title":"Dice in the Black Box: User Experiences with an Inscrutable Algorithm","created":"2018-12-07","cited_by_count":7},{"id":"2306.01615","title":"Enough With \"Human-AI Collaboration\"","created":"2023-06-02","cited_by_count":6},{"id":"1712.09124","title":"Demographics and discussion influence views on algorithmic fairness","created":"2017-12-25","cited_by_count":4},{"id":"2103.14874","title":"Human-in-the-loop Handling of Knowledge Drift","created":"2021-03-27","cited_by_count":4},{"id":"2309.05443","title":"Know What Not To Know: Users' Perception of Abstaining Classifiers","created":"2023-09-11","cited_by_count":1}],"next_cursor":null}