{"total":31,"rows":[{"id":"2305.03514","title":"Can Large Language Models Transform Computational Social Science?","created":"2023-04-12","cited_by_count":250,"area":null},{"id":"2403.03883","title":"SaulLM-7B: A pioneering Large Language Model for Law","created":"2024-03-06","cited_by_count":142,"area":null},{"id":"2108.05542","title":"AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing","created":"2021-08-12","cited_by_count":131,"area":null},{"id":"2110.08534","title":"Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora","created":"2021-10-16","cited_by_count":115,"area":null},{"id":"2404.17790","title":"Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities","created":"2024-04-27","cited_by_count":111,"area":null},{"id":"2309.09530","title":"Adapting Large Language Models to Domains via Reading Comprehension","created":"2023-09-18","cited_by_count":50,"area":null},{"id":"2407.19584","title":"SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain","created":"2024-07-28","cited_by_count":39,"area":null},{"id":"2302.08675","title":"DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction","created":"2023-02-17","cited_by_count":37,"area":null},{"id":"2211.03154","title":"On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey","created":"2022-11-06","cited_by_count":23,"area":null},{"id":"2406.11477","title":"How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?","created":"2024-06-17","cited_by_count":22,"area":null},{"id":"2504.17119","title":"The Rise of Small Language Models in Healthcare: A Comprehensive Survey","created":"2025-04-23","cited_by_count":18,"area":null},{"id":"2210.14867","title":"Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning","created":"2022-10-26","cited_by_count":16,"area":null},{"id":"2403.01467","title":"Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation","created":"2024-03-03","cited_by_count":15,"area":null},{"id":"2211.00430","title":"VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding","created":"2022-11-01","cited_by_count":12,"area":null},{"id":"2412.11704","title":"Adapting Chat Language Models Using Only Target Unlabeled Language Data","created":"2024-12-16","cited_by_count":6,"area":null},{"id":"2601.13260","title":"Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models","created":"2026-01-19","cited_by_count":6,"area":null},{"id":"2311.00408","title":"AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2504.19856","title":"Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language","created":"2025-04-28","cited_by_count":3,"area":null},{"id":"2211.03988","title":"Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2211.08192","title":"RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use","created":"2022-11-15","cited_by_count":2,"area":null},{"id":"2410.21479","title":"TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text","created":"2024-10-28","cited_by_count":2,"area":null},{"id":"2309.17171","title":"Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain","created":"2023-09-29","cited_by_count":1,"area":null},{"id":"2311.03216","title":"Mini Minds: Exploring Bebeshka and Zlata Baby Models","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2403.13681","title":"Ayn: A Tiny yet Competitive Indian Legal Language Model Pretrained from Scratch","created":"2024-03-20","cited_by_count":1,"area":null},{"id":"2509.26124","title":"Vocabulary Customization for Efficient Domain-Specific LLM Deployment","created":"2025-09-30","cited_by_count":1,"area":null},{"id":"2606.28057","title":"MultiHashFormer: Hash-based Generative Language Models","created":"2026-06-26","cited_by_count":1,"area":null},{"id":"2212.01217","title":"Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device","created":"2022-11-03","cited_by_count":0,"area":null},{"id":"2305.14208","title":"Domain Private Transformers for Multi-Domain Dialog Systems","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2506.12403","title":"Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions","created":"2025-06-14","cited_by_count":0,"area":null},{"id":"2509.21192","title":"GEP: A GCG-Based method for extracting personally identifiable information from chatbots built on small language models","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2512.11502","title":"Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extraction","created":"2025-12-12","cited_by_count":0,"area":null}],"next_cursor":null}