# Who Owns the Output? Bridging Law and Technology in LLMs Attribution arXiv: https://arxiv.org/abs/2504.01032 ยท HTML: https://mlsanity.com/paper/2504.01032/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 81 references that are in the map. - First posted on arXiv 29 Mar 2025. ## Builds on (most cited first, 30 of 32) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023) - [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://mlsanity.com/paper/2310.06824/) (2023) - [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024) - [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017) - [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023) - [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018) - [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016) - [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018) - [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023) - [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://mlsanity.com/paper/2204.07931/) (2022) - [Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango](https://mlsanity.com/paper/2209.07686/) (2022) - [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023) - [A Survey on Fairness in Large Language Models](https://mlsanity.com/paper/2308.10149/) (2023) - [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://mlsanity.com/paper/2311.04378/) (2023) - [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023) - [Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics](https://mlsanity.com/paper/2403.14077/) (2024) - [Instructional Fingerprinting of Large Language Models](https://mlsanity.com/paper/2401.12255/) (2024) - [LLMmap: Fingerprinting For Large Language Models](https://mlsanity.com/paper/2407.15847/) (2024) - [A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks](https://mlsanity.com/paper/2408.01319/) (2024) - [Sequential Integrated Gradients: a simple but effective method for explaining language models](https://mlsanity.com/paper/2305.15853/) (2023) - [Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives](https://mlsanity.com/paper/2407.14962/) (2024) - [Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity](https://mlsanity.com/paper/2401.07348/) (2024) - [Watermark-based Attribution of AI-Generated Content](https://mlsanity.com/paper/2404.04254/) (2024) - [Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks](https://mlsanity.com/paper/2303.02836/) (2023) - [A Comprehensive Review on Digital Image Watermarking](https://mlsanity.com/paper/2207.06909/) (2022) ## Built on by (most cited first, 4 of 4) - [Better Training Data Attribution via Better Inverse Hessian-Vector Products](https://mlsanity.com/paper/2507.14740/) (2025) - [Frankentext: Stitching random text fragments into long-form narratives](https://mlsanity.com/paper/2505.18128/) (2025) - [BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web](https://mlsanity.com/paper/2508.13787/) (2025) - [Sovereign Context Protocol: An Open Attribution Layer for Human-Generated Content in the Age of Large Language Models](https://mlsanity.com/paper/2603.27094/) (2026) ## Abstract (arXiv) Since the introduction of ChatGPT in 2022, Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation, enabling the generation of human-quality content, spanning every medium, text, images, videos, and audio. The chances offered by generative AI models are endless and are drastically reducing the time required to generate content and usually raising the quality of the generation. However, considering the complexity and the difficult traceability of the generated content, the use of these tools provides challenges in attributing AI-generated content. The difficult attribution resides for a variety of reasons, starting from the lack of a systematic fingerprinting of the generated content and ending with the enormous amount of data on which LLMs and LMM are trained, which makes it difficult to connect generated content to the training data. This scenario is raising concerns about intellectual property and ethical responsibilities. To address these concerns, in this paper, we bridge the technological, ethical, and legislative aspects, by proposing a review of the legislative and technological instruments today available and proposing a legal framework to ensure accountability. In the end, we propose three use cases of how these can be combined to guarantee that attribution is respected. However, even though the techniques available today can guarantee a greater attribution to a greater extent, strong limitations still apply, that can be solved uniquely by the development of new attribution techniques, to be applied to LLMs and LMMs.