{"total":30,"rows":[{"id":"2402.01613","title":"Nomic Embed: Training a Reproducible Long Context Text Embedder","created":"2024-02-02","cited_by_count":265,"area":null},{"id":"2407.19669","title":"mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval","created":"2024-07-29","cited_by_count":236,"area":null},{"id":"2509.06888","title":"mmBERT: A Modern Multilingual Encoder with Annealed Language Learning","created":"2025-09-08","cited_by_count":45,"area":null},{"id":"2507.11412","title":"Seq vs Seq: An Open Suite of Paired Encoders and Decoders","created":"2025-07-15","cited_by_count":38,"area":null},{"id":"2412.12591","title":"LLMs are Also Effective Embedding Models: An In-depth Overview","created":"2024-12-17","cited_by_count":32,"area":null},{"id":"2506.10896","title":"BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP","created":"2025-06-12","cited_by_count":28,"area":null},{"id":"2504.03964","title":"Clinical ModernBERT: An efficient and long context encoder for biomedical text","created":"2025-04-04","cited_by_count":26,"area":null},{"id":"2504.08716","title":"ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on Transformer Encoder Models Performance","created":"2025-04-11","cited_by_count":12,"area":null},{"id":"2407.16382","title":"TookaBERT: A Step Forward for Persian NLU","created":"2024-07-23","cited_by_count":7,"area":null},{"id":"2502.05790","title":"Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining","created":"2025-02-09","cited_by_count":7,"area":null},{"id":"2410.23261","title":"$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources","created":"2024-10-30","cited_by_count":6,"area":null},{"id":"2409.12175","title":"Expanding Expressivity in Transformer Models with M\\\"obiusAttention","created":"2024-09-08","cited_by_count":4,"area":null},{"id":"2507.20783","title":"On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey","created":"2025-07-28","cited_by_count":3,"area":null},{"id":"2505.15696","title":"MaxPoolBERT: Enhancing BERT Classification via Layer- and Token-Wise Aggregation","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2507.05006","title":"Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search","created":"2025-07-07","cited_by_count":2,"area":null},{"id":"2508.17400","title":"Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs","created":"2025-08-24","cited_by_count":2,"area":null},{"id":"2512.12880","title":"Improving Recursive Transformers with Mixture of LoRAs","created":"2025-12-14","cited_by_count":2,"area":null},{"id":"2512.23065","title":"TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish","created":"2025-12-28","cited_by_count":1,"area":null},{"id":"2601.05776","title":"One Script Instead of Hundreds? On Pretraining Romanized Encoder Language Models","created":"2026-01-09","cited_by_count":1,"area":null},{"id":"2603.12191","title":"Long-Context Encoder Models for Polish Language Understanding","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2502.18009","title":"Patient Trajectory Prediction: Integrating Clinical Notes with Transformers","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2602.06093","title":"NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2602.15814","title":"Avey-B","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2602.22045","title":"DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2603.18676","title":"MANAR: Memory-augmented Attention with Navigational Abstract Conceptual Representation","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2606.28538","title":"Legal Domain Adaptation of Modern BERT Models","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2606.28999","title":"BERTomelo: Your Portuguese Encoder Best Friend","created":"2026-06-27","cited_by_count":0,"area":null},{"id":"2608.03994","title":"When Attention Goes Blind: Numerical Failure in ALiBi Positional Encodings","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.17599","title":"Structure is not mechanism: high-gain gated-FFN rows across text and genomic foundation models","created":"2026-09-13","cited_by_count":0,"area":null},{"id":"2610.15028","title":"UltraBERT: Beyond the Limit of BERT Pre-Training","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}