Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun

TL;DR
The paper introduces CoEM, a lightweight domain adaptation method for large language models that uses user feedback and knowledge snippets to improve domain-specific content generation without extensive retraining.
Contribution
CoEM is a novel framework that constructs a knowledge pool from user contributions and uses interactive dialogues and ratings for efficient domain adaptation of LLMs.
Findings
Significant improvements in domain-specific generation quality.
Validated with 15k user feedback in finance.
Avoids heavy fine-tuning overhead.
Abstract
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…
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Taxonomy
TopicsModel-Driven Software Engineering Techniques
