{"list":"other","total":52,"rows":[{"id":"2110.14168","title":"Training Verifiers to Solve Math Word Problems","created":"2021-10-27","cited_by_count":10109},{"id":"2103.03874","title":"Measuring Mathematical Problem Solving With the MATH Dataset","created":"2021-03-05","cited_by_count":5371},{"id":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"2305.20050","title":"Let's Verify Step by Step","created":"2023-05-31","cited_by_count":3789},{"id":"1809.09600","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","created":"2018-09-25","cited_by_count":3754},{"id":"2406.01574","title":"MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark","created":"2024-06-03","cited_by_count":1848},{"id":"2009.13081","title":"What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams","created":"2020-09-28","cited_by_count":1356},{"id":"2312.08935","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","created":"2023-12-14","cited_by_count":928},{"id":"2211.14275","title":"Solving math word problems with process- and outcome-based feedback","created":"2022-11-25","cited_by_count":901},{"id":"2411.16594","title":"From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge","created":"2024-11-25","cited_by_count":494},{"id":"2502.18864","title":"Accelerating scientific discovery with Co-Scientist","created":"2025-02-26","cited_by_count":401},{"id":"2205.09712","title":"Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning","created":"2022-05-19","cited_by_count":334},{"id":"2412.06559","title":"ProcessBench: Identifying Process Errors in Mathematical Reasoning","created":"2024-12-09","cited_by_count":292},{"id":"2110.01052","title":"Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control","created":"2021-10-03","cited_by_count":271},{"id":"1610.06545","title":"Revisiting Classifier Two-Sample Tests","created":"2016-10-20","cited_by_count":268},{"id":"2406.18403","title":"LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks","created":"2024-06-26","cited_by_count":243},{"id":"2306.10193","title":"Conformal Language Modeling","created":"2023-06-16","cited_by_count":170},{"id":"2210.01948","title":"Game-theoretic statistics and safe anytime-valid inference","created":"2022-10-04","cited_by_count":155},{"id":"2104.08279","title":"Testing for Outliers with Conformal p-values","created":"2021-04-16","cited_by_count":134},{"id":"2402.10978","title":"Language Models with Conformal Factuality Guarantees","created":"2024-02-15","cited_by_count":131},{"id":"2401.12794","title":"Benchmarking LLMs via Uncertainty Quantification","created":"2024-01-23","cited_by_count":126},{"id":"2504.16828","title":"Process Reward Models That Think","created":"2025-04-23","cited_by_count":118},{"id":"2504.08942","title":"AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories","created":"2025-04-11","cited_by_count":108},{"id":"1906.07801","title":"Safe Testing","created":"2019-06-18","cited_by_count":101},{"id":"2406.09714","title":"Large language model validity via enhanced conformal prediction methods","created":"2024-06-14","cited_by_count":92},{"id":"2410.11287","title":"Process Reward Model with Q-Value Rankings","created":"2024-10-15","cited_by_count":80},{"id":"2410.23614","title":"Hypothesis testing with e-values","created":"2024-10-31","cited_by_count":74},{"id":"2404.18021","title":"CRISPR-GPT for Agentic Automation of Gene-editing Experiments","created":"2024-04-27","cited_by_count":58},{"id":"1912.11436","title":"Universal Inference","created":"2019-12-24","cited_by_count":44},{"id":"2502.09858","title":"Automated Hypothesis Validation with Agentic Sequential Falsifications","created":"2025-02-14","cited_by_count":44},{"id":"2110.06177","title":"Tracking the risk of a deployed model and detecting harmful distribution shifts","created":"2021-10-12","cited_by_count":40},{"id":"2210.10768","title":"Anytime-valid off-policy inference for contextual bandits","created":"2022-10-19","cited_by_count":37},{"id":"2102.10439","title":"Retrain or not retrain: Conformal test martingales for change-point detection","created":"2021-02-20","cited_by_count":32},{"id":"2203.03532","title":"E-detectors: a nonparametric framework for sequential change detection","created":"2022-03-07","cited_by_count":32},{"id":"2010.09851","title":"Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference","created":"2020-10-19","cited_by_count":31},{"id":"2412.21154","title":"Aviary: training language agents on challenging scientific tasks","created":"2024-12-30","cited_by_count":26},{"id":"2305.17570","title":"Auditing Fairness by Betting","created":"2023-05-27","cited_by_count":25},{"id":"1603.09000","title":"Online Rules for Control of False Discovery Rate and False Discovery Exceedance","created":"2016-03-29","cited_by_count":21},{"id":"2505.18404","title":"Thought calibration: Efficient and confident test-time scaling","created":"2025-05-23","cited_by_count":21},{"id":"2506.09338","title":"Know What You Don't Know: Uncertainty Calibration of Process Reward Models","created":"2025-06-11","cited_by_count":21},{"id":"2505.04608","title":"WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales","created":"2025-05-07","cited_by_count":16},{"id":"2304.02611","title":"Randomized and Exchangeable Improvements of Markov's, Chebyshev's and Chernoff's Inequalities","created":"2023-04-05","cited_by_count":15},{"id":"2507.12948","title":"Probabilistic Soundness Guarantees in LLM Reasoning Chains","created":"2025-07-17","cited_by_count":13},{"id":"1905.11465","title":"ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls","created":"2019-05-27","cited_by_count":11},{"id":"2410.22318","title":"Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting","created":"2024-10-29","cited_by_count":11},{"id":"2507.08721","title":"Monitoring Risks in Test-Time Adaptation","created":"2025-07-11","cited_by_count":11},{"id":"2412.12910","title":"Sequential Harmful Shift Detection Without Labels","created":"2024-12-17","cited_by_count":10},{"id":"2506.16416","title":"On Continuous Monitoring of Risk Violations under Unknown Shift","created":"2025-06-19","cited_by_count":10},{"id":"2505.00292","title":"Offline changepoint localization using a matrix of conformal p-values","created":"2025-05-01","cited_by_count":8},{"id":"2501.11866","title":"Evaluating multiple models using labeled and unlabeled data","created":"2025-01-21","cited_by_count":4},{"id":"2310.03722","title":"Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance","created":"2023-10-05","cited_by_count":2},{"id":"2510.25770","title":"E-Scores for (In)Correctness Assessment of Generative Model Outputs","created":"2025-10-29","cited_by_count":2}],"next_cursor":null}