mlsanityData to 14 Oct 2026

Collaborative Editable Model

arXiv 2506.14146 ↗First posted 17 Jun 2025cs.AIcited by 0

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Abstract (arXiv)

Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues combined with user ratings and attribution analysis to pinpoint high-value knowledge fragments, and injects these fragments via in-context prompts for lightweight domain adaptation. With high-value knowledge, the LLM can generate more accurate and domain-specific content. In a financial information scenario, we collect 15k feedback from about 120 users and validate CoEM with user ratings to assess the quality of generated insights, demonstrating significant improvements in domain-specific generation while avoiding the time and compute overhead of traditional fine-tuning workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 17 Jun 2025.

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References · 27

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  5. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,565
  6. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  7. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 759
  8. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 264
  9. 2023Large Language Models in Finance: A Surveycited by 226
  10. 2024When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Methodcited by 204
  11. 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
  12. 2022Domain Adaptation via Prompt Learningcited by 128
15 more
  1. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  2. 2025HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptationcited by 114
  3. 2025Towards Scientific Intelligence: A Survey of LLM-based Scientific Agentscited by 98
  4. 2024On Protecting the Data Privacy of Large Language Models (LLMs): A Surveycited by 96
  5. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  6. 2024A Survey on LoRA of Large Language Modelscited by 67
  7. 2022PODA: Prompt-driven Zero-shot Domain Adaptationcited by 60
  8. 2024LegalAgentBench: Evaluating LLM Agents in Legal Domaincited by 59
  9. 2023Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaborationcited by 52
  10. 2025Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Surveycited by 51
  11. 2023Prompt-based Distribution Alignment for Unsupervised Domain Adaptationcited by 44
  12. 2024Fine-tuning Large Language Models for Domain-specific Machine Translationcited by 43
  13. 2024D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Modelscited by 41
  14. 2024Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuningcited by 31
  15. 2022Domain Generalization using Pretrained Models without Fine-tuningcited by 29

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