{"total":20,"rows":[{"id":"2402.09739","title":"QuRating: Selecting High-Quality Data for Training Language Models","created":"2024-02-15","cited_by_count":157,"area":null},{"id":"2502.10341","title":"Organize the Web: Constructing Domains Enhances Pre-Training Data Curation","created":"2025-02-14","cited_by_count":76,"area":null},{"id":"2403.12958","title":"Dated Data: Tracing Knowledge Cutoffs in Large Language Models","created":"2024-03-19","cited_by_count":51,"area":null},{"id":"2410.05613","title":"Stereotype or Personalization? User Identity Biases Chatbot Recommendations","created":"2024-10-08","cited_by_count":34,"area":null},{"id":"2406.03198","title":"The Impossibility of Fair LLMs","created":"2024-05-28","cited_by_count":22,"area":null},{"id":"2405.08209","title":"Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp","created":"2024-05-13","cited_by_count":15,"area":null},{"id":"2410.12029","title":"On Classification with Large Language Models in Cultural Analytics","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2410.03884","title":"KidLM: Advancing Language Models for Children -- Early Insights and Future Directions","created":"2024-10-04","cited_by_count":8,"area":null},{"id":"2405.01286","title":"Data Feminism for AI","created":"2024-05-02","cited_by_count":6,"area":null},{"id":"2409.13852","title":"Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs","created":"2024-09-20","cited_by_count":5,"area":null},{"id":"2510.16713","title":"so much depends / upon / a whitespace: Why Whitespace Matters for Poets and LLMs","created":"2025-10-19","cited_by_count":4,"area":null},{"id":"2410.13114","title":"Sound Check: Auditing Audio Datasets","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2505.14070","title":"Enhancing LLMs via High-Knowledge Data Selection","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2606.19468","title":"Characterizing Narrative Content in Web-scale LLM Pretraining Data","created":"2026-06-17","cited_by_count":2,"area":null},{"id":"2504.12495","title":"Beyond Text: Characterizing Domain Expert Needs in Document Research","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2505.21378","title":"Analyzing values about gendered language reform in LLMs' revisions","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2608.09093","title":"The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2505.17345","title":"The Case for Repeatable, Open, and Expert-Grounded Hallucination Benchmarks in Large Language Models","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2606.07778","title":"Unlocking Latent Value: Taxonomy-Guided Recovery of High-Performing Data from Low-Tier Web Corpora","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2610.13321","title":"Host-Aware Web Crawling for LLM Pretraining Data","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}