{"total":21,"rows":[{"id":"2407.08818","title":"MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization","created":"2024-07-11","cited_by_count":31,"area":null},{"id":"2310.16712","title":"LLM Performance Predictors are good initializers for Architecture Search","created":"2023-10-25","cited_by_count":29,"area":null},{"id":"2403.03814","title":"Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ","created":"2024-03-06","cited_by_count":27,"area":null},{"id":"2311.00522","title":"Text Rendering Strategies for Pixel Language Models","created":"2023-11-01","cited_by_count":22,"area":null},{"id":"2401.03321","title":"PIXAR: Auto-Regressive Language Modeling in Pixel Space","created":"2024-01-06","cited_by_count":16,"area":null},{"id":"2505.21265","title":"Multilingual Pretraining for Pixel Language Models","created":"2025-05-27","cited_by_count":12,"area":null},{"id":"2404.10710","title":"Autoregressive Pre-Training on Pixels and Texts","created":"2024-04-16","cited_by_count":10,"area":null},{"id":"2411.17666","title":"How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations","created":"2024-11-26","cited_by_count":9,"area":null},{"id":"2512.11437","title":"CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare","created":"2025-12-12","cited_by_count":9,"area":null},{"id":"2507.12720","title":"FLEXITOKENS: Flexible Tokenization for Evolving Language Models","created":"2025-07-17","cited_by_count":8,"area":null},{"id":"2311.09808","title":"PixT3: Pixel-based Table-To-Text Generation","created":"2023-11-16","cited_by_count":6,"area":null},{"id":"2408.04628","title":"LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP","created":"2024-08-08","cited_by_count":6,"area":null},{"id":"2504.02122","title":"Overcoming Vocabulary Constraints with Pixel-level Fallback","created":"2025-04-02","cited_by_count":6,"area":null},{"id":"2507.04886","title":"Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations","created":"2025-07-07","cited_by_count":5,"area":null},{"id":"2502.12476","title":"CoCo-CoLa: Evaluating and Improving Language Adherence in Multilingual LLMs","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2602.04289","title":"Proxy Compression for Language Modeling","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2405.19290","title":"Integrating Multi-scale Contextualized Information for Byte-based Neural Machine Translation","created":"2024-05-29","cited_by_count":1,"area":null},{"id":"2409.05021","title":"Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation","created":"2024-09-08","cited_by_count":1,"area":null},{"id":"2601.09566","title":"Hot-Start Chinese Language Modeling:Visual Glyphs Accelerate Sample-Efficient Learning","created":"2026-01-14","cited_by_count":1,"area":null},{"id":"2604.11575","title":"MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2411.01474","title":"MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation","created":"2024-11-03","cited_by_count":0,"area":null}],"next_cursor":null}