{"total":21,"rows":[{"id":"2402.04333","title":"LESS: Selecting Influential Data for Targeted Instruction Tuning","created":"2024-02-06","cited_by_count":532,"area":null},{"id":"2404.07965","title":"Rho-1: Not All Tokens Are What You Need","created":"2024-04-11","cited_by_count":133,"area":null},{"id":"2402.05123","title":"A Survey on Data Selection for LLM Instruction Tuning","created":"2024-02-04","cited_by_count":77,"area":null},{"id":"2410.23771","title":"What is Wrong with Perplexity for Long-context Language Modeling?","created":"2024-10-31","cited_by_count":69,"area":null},{"id":"2402.16705","title":"SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection","created":"2024-02-26","cited_by_count":34,"area":null},{"id":"2405.00705","title":"SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning","created":"2024-04-23","cited_by_count":19,"area":null},{"id":"2505.17505","title":"L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models","created":"2025-05-23","cited_by_count":15,"area":null},{"id":"2408.14438","title":"Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study","created":"2024-08-26","cited_by_count":12,"area":null},{"id":"2402.12146","title":"Enabling Weak LLMs to Judge Response Reliability via Meta Ranking","created":"2024-02-19","cited_by_count":9,"area":null},{"id":"2410.05090","title":"HyperINF: Unleashing the HyperPower of the Schulz's Method for Data Influence Estimation","created":"2024-10-07","cited_by_count":7,"area":null},{"id":"2508.21589","title":"Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning","created":"2025-08-29","cited_by_count":7,"area":null},{"id":"2502.12594","title":"PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2605.30288","title":"MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection","created":"2026-05-28","cited_by_count":3,"area":null},{"id":"2412.09990","title":"Small Language Model as Data Prospector for Large Language Model","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2502.16159","title":"ZiGong 1.0: A Large Language Model for Financial Credit","created":"2025-02-22","cited_by_count":2,"area":null},{"id":"2509.06524","title":"LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection","created":"2025-09-08","cited_by_count":2,"area":null},{"id":"2410.11540","title":"Data Quality Control in Federated Instruction-tuning of Large Language Models","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2602.13773","title":"On Representation Redundancy in Large-Scale Instruction Tuning Data Selection","created":"2026-02-14","cited_by_count":1,"area":null},{"id":"2405.20852","title":"Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2411.14121","title":"Learning from \"Silly\" Questions Improves Large Language Models, But Only Slightly","created":"2024-11-21","cited_by_count":0,"area":null},{"id":"2605.09665","title":"Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies","created":"2026-05-10","cited_by_count":0,"area":null}],"next_cursor":null}