{"total":86,"rows":[{"id":"2309.05196","title":"Does Writing with Language Models Reduce Content Diversity?","created":"2023-09-11","cited_by_count":192,"area":null},{"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":"2304.14293","title":"Controlled Text Generation with Natural Language Instructions","created":"2023-04-27","cited_by_count":87,"area":null},{"id":"2210.07321","title":"Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods","created":"2022-10-13","cited_by_count":79,"area":null},{"id":"2205.09726","title":"RankGen: Improving Text Generation with Large Ranking Models","created":"2022-05-19","cited_by_count":72,"area":null},{"id":"2302.04460","title":"Bag of Tricks for Training Data Extraction from Language Models","created":"2023-02-09","cited_by_count":70,"area":null},{"id":"2305.14613","title":"Selectively Answering Ambiguous Questions","created":"2023-05-24","cited_by_count":66,"area":null},{"id":"2408.17355","title":"Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling","created":"2024-08-30","cited_by_count":52,"area":null},{"id":"2305.11707","title":"What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability","created":"2023-05-19","cited_by_count":51,"area":null},{"id":"2307.10236","title":"Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models","created":"2023-07-16","cited_by_count":41,"area":null},{"id":"2310.10226","title":"Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective","created":"2023-10-16","cited_by_count":39,"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":"2310.09139","title":"The Consensus Game: Language Model Generation via Equilibrium Search","created":"2023-10-13","cited_by_count":36,"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":"2305.15065","title":"Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning","created":"2023-05-24","cited_by_count":33,"area":null},{"id":"2310.01693","title":"Closing the Curious Case of Neural Text Degeneration","created":"2023-10-02","cited_by_count":32,"area":null},{"id":"2407.02961","title":"Towards a Scalable Reference-Free Evaluation of Generative Models","created":"2024-07-03","cited_by_count":26,"area":null},{"id":"2308.15202","title":"Benchmarking the Generation of Fact Checking Explanations","created":"2023-08-29","cited_by_count":25,"area":null},{"id":"2402.18223","title":"Improving Open-Ended Text Generation via Adaptive Decoding","created":"2024-02-28","cited_by_count":21,"area":null},{"id":"2310.10299","title":"Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control","created":"2023-10-16","cited_by_count":20,"area":null},{"id":"2305.12680","title":"G3Detector: General GPT-Generated Text Detector","created":"2023-05-22","cited_by_count":17,"area":null},{"id":"2307.03749","title":"On the Efficacy of Sampling Adapters","created":"2023-07-07","cited_by_count":17,"area":null},{"id":"2305.16958","title":"MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies","created":"2023-05-26","cited_by_count":16,"area":null},{"id":"2407.07412","title":"Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation","created":"2024-07-10","cited_by_count":13,"area":null},{"id":"2505.19371","title":"Foundations of Top-$k$ Decoding For Language Models","created":"2025-05-25","cited_by_count":10,"area":null},{"id":"2506.23601","title":"Semantic-guided Diverse Decoding for Large Language Model","created":"2025-06-30","cited_by_count":9,"area":null},{"id":"2512.15586","title":"Bolmo: Byteifying the Next Generation of Language Models","created":"2025-12-17","cited_by_count":9,"area":null},{"id":"2302.06784","title":"The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation","created":"2023-02-14","cited_by_count":8,"area":null},{"id":"2305.18294","title":"Transformer Language Models Handle Word Frequency in Prediction Head","created":"2023-05-29","cited_by_count":8,"area":null},{"id":"2310.04691","title":"EMO: Earth Mover Distance Optimization for Auto-Regressive Language Modeling","created":"2023-10-07","cited_by_count":7,"area":null},{"id":"2410.03968","title":"Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies","created":"2024-10-04","cited_by_count":7,"area":null},{"id":"2211.11924","title":"Best-$k$ Search Algorithm for Neural Text Generation","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2305.04782","title":"HistAlign: Improving Context Dependency in Language Generation by Aligning with History","created":"2023-05-08","cited_by_count":6,"area":null},{"id":"2210.07228","title":"Language Model Decoding as Likelihood-Utility Alignment","created":"2022-10-13","cited_by_count":5,"area":null},{"id":"2309.10707","title":"Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models","created":"2023-09-18","cited_by_count":5,"area":null},{"id":"2407.03646","title":"Differentiating Between Human-Written and AI-Generated Texts Using Automatically Extracted Linguistic Features","created":"2024-07-04","cited_by_count":5,"area":null},{"id":"2507.10894","title":"NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization","created":"2025-07-15","cited_by_count":5,"area":null},{"id":"2602.00782","title":"Controlling Repetition in Protein Language Models","created":"2026-01-31","cited_by_count":5,"area":null},{"id":"2605.09995","title":"Annotations Mitigate Post-Training Mode Collapse","created":"2026-05-11","cited_by_count":5,"area":null},{"id":"2306.03061","title":"Structured Voronoi Sampling","created":"2023-06-05","cited_by_count":4,"area":null},{"id":"2406.10267","title":"Unused information in token probability distribution of generative LLM: improving LLM reading comprehension through calculation of expected values","created":"2024-06-11","cited_by_count":4,"area":null},{"id":"2506.05387","title":"Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs","created":"2025-06-03","cited_by_count":4,"area":null},{"id":"2602.16162","title":"LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2308.15334","title":"The Responsible Development of Automated Student Feedback with Generative AI","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2311.04954","title":"Prompt Sketching for Large Language Models","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2410.01064","title":"Truth or Deceit? A Bayesian Decoding Game Enhances Consistency and Reliability","created":"2024-10-01","cited_by_count":3,"area":null},{"id":"2502.14037","title":"DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation","created":"2025-02-19","cited_by_count":3,"area":null},{"id":"2507.21086","title":"Multi-Amateur Contrastive Decoding for Text Generation","created":"2025-06-22","cited_by_count":3,"area":null},{"id":"2602.10346","title":"Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models","created":"2026-02-10","cited_by_count":3,"area":null},{"id":"2603.18482","title":"The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices","created":"2026-03-19","cited_by_count":3,"area":null},{"id":"2504.21020","title":"Context-Enhanced Contrastive Search for Improved LLM Text Generation","created":"2025-04-22","cited_by_count":2,"area":null},{"id":"2507.10419","title":"Multiple Choice Learning of Low-Rank Adapters for Language Modeling","created":"2025-07-14","cited_by_count":2,"area":null},{"id":"2510.13103","title":"ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models","created":"2025-10-15","cited_by_count":2,"area":null},{"id":"2604.02319","title":"No Single Best Model for Diversity: Learning a Router for Sample Diversity","created":"2026-04-02","cited_by_count":2,"area":null},{"id":"2605.11128","title":"Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2608.19893","title":"Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models","created":"2026-08-20","cited_by_count":2,"area":null},{"id":"2310.16992","title":"How well can machine-generated texts be identified and can language models be trained to avoid identification?","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2405.19299","title":"Expert-Guided Extinction of Toxic Tokens for Debiased Generation","created":"2024-05-29","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":"2503.07595","title":"Detection Avoidance Techniques for Large Language Models","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2503.23924","title":"Model Hemorrhage and the Robustness Limits of Large Language Models","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2508.13580","title":"A Comparative Study of Decoding Strategies in Medical Text Generation","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2510.12343","title":"Traveling Salesman-Based Token Ordering Improves Stability in Homomorphically Encrypted Language Models","created":"2025-10-14","cited_by_count":1,"area":null},{"id":"2601.10267","title":"In-Context Source and Channel Coding","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2603.05352","title":"Ailed: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2603.14567","title":"Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes","created":"2026-03-15","cited_by_count":1,"area":null},{"id":"2603.24989","title":"Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2211.03648","title":"Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems","created":"2022-11-07","cited_by_count":0,"area":null},{"id":"2305.00217","title":"Still no evidence for an effect of the proportion of non-native speakers on language complexity -- A response to Kauhanen, Einhaus & Walkden (2023)","created":"2023-04-29","cited_by_count":0,"area":null},{"id":"2512.11919","title":"A fine-grained look at causal effects in causal spaces","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.00377","title":"DecompressionLM: Deterministic, Diagnostic, and Zero-Shot Concept Graph Extraction from Language Models","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2605.00435","title":"Escaping Mode Collapse in LLM Generation via Geometric Regulation","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.04400","title":"Contextual Memory-Enhanced Source Coding for Low-SNR Communications","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.11188","title":"Adversarial SQL Injection Generation with LLM-Based Architectures","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.24667","title":"When Mean CE Fails: Median CE Can Better Track Language Model Quality","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2606.01451","title":"Before and After Temperature: A Distributional View of Creative LLM Generation","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.20714","title":"A Generalized Formalism of Auto-Regressive Decoding for Speech Processing","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.20791","title":"Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.22758","title":"XTC: Head-Aware Sampling by Excluding Top Choices","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.22761","title":"Don't Repeat Yourself: Stopping Verbatim Loops at Sampling Time","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.26168","title":"Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.29903","title":"When Less is More: Understanding When Token Filtering Helps and Fails in AI-generated Text Detection","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.34992","title":"Composable Decoding on the Probability Simplex: Theory and Implementation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.00888","title":"Match the Distribution, Not the Compute: Post-Training Multi-Token Prediction Heads","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.15058","title":"The Optimal Decoder is Tilted Temperature","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.15570","title":"Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}