We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content (e.g. code, news articles, short-form videos) among creators whose intellectual property appears in the context window. Our approach is based on the least core solution concept from cooperative game theory, which distributes value in a way that is as stable as possible by ensuring that no subset of creators is significantly under-compensated relative to the value they could generate on their own. We develop algorithms for approximating the least core, which leverage novel routines for constraint seeding and constraint separation. On a web retrieval credit assignment task, we find that our approaches are capable of approximating the least core using orders of magnitude fewer LLM calls compared to alternative methods.

翻译：我们提出了一种激励兼容的上下文信用分配机制：该任务旨在为人工智能生成内容（如代码、新闻文章、短视频）中出现的知识产权所涉及的创作者分配信用。我们的方法基于合作博弈论中的最小核心解概念，该概念通过确保没有任何创作者子集相对于其独立创造的价值被显著低估，从而实现价值分配的稳定性最大化。我们开发了用于近似最小核心的算法，这些算法利用了约束播种和约束分离的新型例行程序。在网页检索信用分配任务中，我们发现与替代方法相比，我们的方法能够通过数量级更少的LLM调用次数来近似最小核心。