Neural Information Processing Systems http://nips.cc/

Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information providers are compensated. To sustain this ecosystem, we need fair mechanisms to attribute and compensate content providers based on their contributions to generated answers. We introduce MaxShapley, an efficient algorithm for fair attribution in generative search pipelines that use retrieval-augmented generation (RAG). MaxShapley is a special case of the celebrated Shapley value; it leverages a decomposable max-sum utility function to compute attributions with linear computation in the number of documents, as opposed to the exponential cost of Shapley values. We evaluate MaxShapley on three multi-hop QA datasets (HotPotQA, MuSiQUE, MS MARCO); MaxShapley achieves comparable attribution quality to exact Shapley computation, while consuming a fraction of its tokens--for instance, it gives up to an 8x reduction in resource consumption over prior state-of-the-art methods at the same attribution accuracy.

Neural Information Processing Systems http://nips.cc/

KDDI enters into'responsible' AI agreement with Google KDDI said its AI service, which will be launched in spring 2026, will "protect the rights of content providers." Major Japanese telecommunications firm KDDI signed an agreement Tuesday with Google Cloud Japan in a bid to develop a "responsible" AI search service that only shows content that creators have given consent to. The agreement would allow KDDI to harness Google's AI assistant Gemini and AI-optimized research tool NotebookLM. "We will promote'responsible AI' that uses AI ethically, legally, and appropriately, and provide an environment where content providers and customers can use AI safely and securely," the company said in a statement. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

The emergence of generative artificial intelligence has put the issue of compensation for content producers back on the table. Generative AI offers undeniable benefits but raises familiar fears tied to disruptive technologies. Legal battles are already emerging worldwide, with intellectual property owners and AI developers clashing over rights. Alongside these legal and ethical concerns lies the economic question: how should revenues generated by AI be fairly distributed? Individual contributions to AI-generated outputs are often too complex to quantify, making it difficult to apply the principle of proportional remuneration, which holds that payment for an individual work is tied to the revenue it generates.

This paper aims to introduce and analyze the Viz system in a comprehensive way, a novel system architecture that integrates Quantized Low-Rank Adapters (QLoRA) to fine-tune large language models (LLM) within a legally compliant and resource efficient marketplace. Viz represents a significant contribution to the field of artificial intelligence, particularly in addressing the challenges of computational efficiency, legal compliance, and economic sustainability in the utilization and monetization of LLMs. The paper delineates the scholarly discourse and developments that have informed the creation of Viz, focusing primarily on the advancements in LLM models, copyright issues in AI training (NYT case, 2023), and the evolution of model fine-tuning techniques, particularly low-rank adapters and quantized low-rank adapters, to create a sustainable and economically compliant framework for LLM utilization. The economic model it proposes benefits content creators, AI developers, and end-users, delineating a harmonious integration of technology, economy, and law, offering a comprehensive solution to the complex challenges of today's AI landscape.