{"total":54,"rows":[{"id":"2203.11171","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","created":"2022-03-21","cited_by_count":5708,"area":"lr"},{"id":"2102.01454","title":"MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers","created":"2021-02-02","cited_by_count":470,"area":null},{"id":"2110.01691","title":"AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts","created":"2021-10-04","cited_by_count":268,"area":null},{"id":"2403.06963","title":"The pitfalls of next-token prediction","created":"2024-03-11","cited_by_count":160,"area":null},{"id":"2205.11822","title":"Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations","created":"2022-05-24","cited_by_count":159,"area":"lr"},{"id":"2406.16838","title":"From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models","created":"2024-06-24","cited_by_count":137,"area":null},{"id":"2210.15191","title":"Truncation Sampling as Language Model Desmoothing","created":"2022-10-27","cited_by_count":123,"area":null},{"id":"2310.04363","title":"Amortizing intractable inference in large language models","created":"2023-10-06","cited_by_count":97,"area":null},{"id":"2206.02369","title":"Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation","created":"2022-06-06","cited_by_count":94,"area":null},{"id":"2203.15721","title":"On Decoding Strategies for Neural Text Generators","created":"2022-03-29","cited_by_count":90,"area":null},{"id":"2405.20971","title":"Amortizing intractable inference in diffusion models for vision, language, and control","created":"2024-05-31","cited_by_count":72,"area":"diffusion"},{"id":"2303.00855","title":"Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents","created":"2023-03-01","cited_by_count":67,"area":"agents"},{"id":"2009.07839","title":"Text Generation by Learning from Demonstrations","created":"2020-09-16","cited_by_count":64,"area":"offlinerl"},{"id":"2104.05336","title":"Machine Translation Decoding beyond Beam Search","created":"2021-04-12","cited_by_count":55,"area":null},{"id":"2212.14578","title":"MAUVE Scores for Generative Models: Theory and Practice","created":"2022-12-30","cited_by_count":36,"area":null},{"id":"2402.13512","title":"From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers","created":"2024-02-21","cited_by_count":34,"area":null},{"id":"2212.10502","title":"A Measure-Theoretic Characterization of Tight Language Models","created":"2022-12-20","cited_by_count":33,"area":null},{"id":"2211.08714","title":"Reward Gaming in Conditional Text Generation","created":"2022-11-16","cited_by_count":30,"area":null},{"id":"2109.13986","title":"Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics","created":"2021-09-28","cited_by_count":27,"area":null},{"id":"2407.18698","title":"Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation","created":"2024-07-26","cited_by_count":27,"area":null},{"id":"2305.01723","title":"Stance Detection: A Practical Guide to Classifying Political Beliefs in Text","created":"2023-05-02","cited_by_count":20,"area":null},{"id":"2203.12788","title":"Evaluating Distributional Distortion in Neural Language Modeling","created":"2022-03-24","cited_by_count":19,"area":null},{"id":"2203.17217","title":"On the probability-quality paradox in language generation","created":"2022-03-31","cited_by_count":16,"area":null},{"id":"2401.02749","title":"Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding","created":"2024-01-05","cited_by_count":15,"area":null},{"id":"2204.03084","title":"Knowledge Infused Decoding","created":"2022-04-06","cited_by_count":12,"area":null},{"id":"2311.15208","title":"LongStory: Coherent, Complete and Length Controlled Long story Generation","created":"2023-11-26","cited_by_count":12,"area":null},{"id":"2104.14279","title":"How (Non-)Optimal is the Lexicon?","created":"2021-04-29","cited_by_count":11,"area":null},{"id":"2311.05263","title":"Model-Based Minimum Bayes Risk Decoding for Text Generation","created":"2023-11-09","cited_by_count":11,"area":null},{"id":"2112.08914","title":"Characterizing and addressing the issue of oversmoothing in neural autoregressive sequence modeling","created":"2021-12-16","cited_by_count":10,"area":null},{"id":"2410.22296","title":"Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks","created":"2024-10-29","cited_by_count":10,"area":null},{"id":"2508.20757","title":"GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation","created":"2025-08-28","cited_by_count":10,"area":null},{"id":"2505.13111","title":"Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation","created":"2025-05-19","cited_by_count":9,"area":"distill"},{"id":"2104.12918","title":"Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News","created":"2021-04-27","cited_by_count":8,"area":null},{"id":"2409.18618","title":"Model-based Preference Optimization in Abstractive Summarization without Human Feedback","created":"2024-09-27","cited_by_count":7,"area":"alignment"},{"id":"2211.11924","title":"Best-$k$ Search Algorithm for Neural Text Generation","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2409.17730","title":"Autoregressive Generation Strategies for Top-K Sequential Recommendations","created":"2024-09-26","cited_by_count":5,"area":null},{"id":"2112.08670","title":"Amortized Noisy Channel Neural Machine Translation","created":"2021-12-16","cited_by_count":4,"area":null},{"id":"2307.01542","title":"Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation","created":"2023-07-04","cited_by_count":3,"area":null},{"id":"2404.03577","title":"Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models","created":"2024-04-04","cited_by_count":3,"area":"lr"},{"id":"2205.04259","title":"Multi-segment preserving sampling for deep manifold sampler","created":"2022-05-09","cited_by_count":2,"area":null},{"id":"2308.13696","title":"On the Depth between Beam Search and Exhaustive Search for Text Generation","created":"2023-08-25","cited_by_count":2,"area":null},{"id":"2505.23540","title":"Probability-Consistent Preference Optimization for Enhanced LLM Reasoning","created":"2025-05-29","cited_by_count":2,"area":"lr"},{"id":"2106.05459","title":"Mode recovery in neural autoregressive sequence modeling","created":"2021-06-10","cited_by_count":1,"area":null},{"id":"2308.08442","title":"Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction","created":"2023-08-16","cited_by_count":1,"area":null},{"id":"2310.12942","title":"On the Representational Capacity of Recurrent Neural Language Models","created":"2023-10-19","cited_by_count":1,"area":null},{"id":"2502.11723","title":"Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption","created":"2025-02-17","cited_by_count":1,"area":null},{"id":"2510.19778","title":"GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2210.00660","title":"A Non-monotonic Self-terminating Language Model","created":"2022-10-03","cited_by_count":0,"area":null},{"id":"2406.10203","title":"A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors","created":"2024-06-14","cited_by_count":0,"area":"alignment"},{"id":"2410.09999","title":"Leveraging Customer Feedback for Multi-modal Insight Extraction","created":"2024-10-13","cited_by_count":0,"area":null},{"id":"2606.21869","title":"The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference","created":"2026-06-20","cited_by_count":0,"area":null},{"id":"2608.25761","title":"Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models","created":"2026-08-26","cited_by_count":0,"area":"lr"},{"id":"2609.26087","title":"The Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scale","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.39566","title":"From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning","created":"2026-09-30","cited_by_count":0,"area":"lr"}],"next_cursor":null}