{"total":16,"rows":[{"id":"2402.16041","title":"Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy","created":"2024-02-25","cited_by_count":20,"area":null},{"id":"2404.03626","title":"Training LLMs over Neurally Compressed Text","created":"2024-04-04","cited_by_count":17,"area":null},{"id":"2410.01660","title":"Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering","created":"2024-10-02","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":"2402.18734","title":"Priority Sampling of Large Language Models for Compilers","created":"2024-02-28","cited_by_count":8,"area":null},{"id":"2509.23234","title":"p-less Sampling: A Robust Hyperparameter-Free Approach for LLM Decoding","created":"2025-09-27","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":"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":"2411.06251","title":"Quasi-random Multi-Sample Inference for Large Language Models","created":"2024-11-09","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":"2606.27359","title":"When are likely answers right? On Sequence Probability and Correctness in LLMs","created":"2026-06-25","cited_by_count":4,"area":null},{"id":"2602.18292","title":"Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers","created":"2026-02-20","cited_by_count":2,"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":"2609.33038","title":"Improving the Diversity of LLM Outputs without a Trade-off","created":"2026-09-27","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":"2610.15570","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}