arXiv:2505.23842v5 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly power search engines and AI assistants that retrieve and summarize content from many sources. By serving answers directly, these systems obscure the original content creators' contributions, threatening the compensation that sustains a healthy content ecosystem. We frame this as a problem of fair document valuation and compensation, and propose a framework based on the Shapley value. Because exact Shapley computation is prohibitively expensive at scale, we develop Cluster Shapley, an approximation tha
The proliferation of LLMs in content aggregation and summarization has intensified concerns about fair compensation for original creators, making this a timely and critical issue.
This research addresses a fundamental economic challenge in the generative AI era, proposing a quantifiable method for attributing value to source documents, which could reshape content economics.
The proposed Shapley-value-based framework offers a potential mechanism for transparent and fair value distribution to content creators, moving beyond current opaque LLM aggregation practices.
- · Original Content Creators
- · Fair Use Advocates
- · LLM Providers (implementing ethical frameworks)
- · Content Licensing Platforms
- · LLM Providers (unwilling to compensate)
- · Black-box Aggregators
- · Traditional Search Engines (if they don't adapt)
Content creators gain a theoretical framework for demanding compensation from LLM aggregators.
New business models emerge for content licensing and data syndication, driven by transparent valuation metrics.
Legal precedents are set regarding intellectual property and fair compensation within AI-driven content generation, potentially leading to new regulatory landscapes.
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Read at arXiv cs.CL