Efficient Decentralized Multi-task Dataset Valuation via Model Merging
Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poo
Record details
Published: 3 July 2026
Source: arXiv
Category: Research
Topics: Transparency · Finance, VC & PE
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
arXiv cs.CR (AI security) · 1 July 2026
From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design
arXiv · 15 June 2026
AI-Powered Browsers Are Broadly Accurate News Summarizers That Reduce Political Bias and Negative Affect
arXiv · 21 July 2026
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation
arXiv cs.AI · 21 July 2026
AI-Powered Browsers Are Broadly Accurate News Summarizers That Reduce Political Bias and Negative Affect
arXiv cs.CY · 22 July 2026
SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity
arXiv · 14 June 2026
How to cite this record
ethics.ai (3 July 2026), “Efficient Decentralized Multi-task Dataset Valuation via Model Merging,” evidence record 274, https://ethics.ai/record/274 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.