Metraipolis › archive › dc_030271e2cf39

LLM eBay: A Simple and Truthful Marketplace for Fine-Tuned LLMs Trading — arXiv:2610.39347

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39347
License (as archived): CC0-1.0
Archived: 2026-11-01T03:00:44.299Z
SHA-256 of the archived text: cdedca284b1b85b5fbf767a3eb1140b43ae16c218ae1e0374926edc3c4e2b9bf
Stable URL of this page: https://metraipolis.com/documento/dc_030271e2cf39

How to cite

LLM eBay: A Simple and Truthful Marketplace for Fine-Tuned LLMs Trading — arXiv:2610.39347 — arXiv (en), https://arxiv.org/abs/2610.39347 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_030271e2cf39 (Metraipolis archive dc_030271e2cf39, sha256 cdedca284b1b85b5fbf767a3eb1140b43ae16c218ae1e0374926edc3c4e2b9bf)

BibTeX

@misc{dc030271e2cf39,
  title = {LLM eBay: A Simple and Truthful Marketplace for Fine-Tuned LLMs Trading — arXiv:2610.39347},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39347},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_030271e2cf39; sha256 cdedca284b1b85b5fbf767a3eb1140b43ae16c218ae1e0374926edc3c4e2b9bf},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39347. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Personalized fine-tuning of Large Language Models (LLMs) enhances user experience but incurs substantial computational costs. Recent studies show that initializing training from a style-similar fine-tuned model reduces adaptation overhead, turning such checkpoints into reusable, tradable assets. A marketplace for these checkpoints, however, faces information asymmetry: sellers may misreport a checkpoint's style, while buyers cannot verify its usefulness before purchase, which invites adverse selection. We introduce LLM eBay, a simple single-round marketplace for trading fine-tuned LLMs. Sellers are paid per purchase at a posted price, and a truncated quadratic proper scoring rule adjusts each payment according to how well the seller's style report agrees with a style signal that the platform measures by generating text on hidden prompts. We show that the score elicits the verifier's expected signal, characterize exactly when truthful reporting is a seller best response under per-purchase demand, and bound the downside risk from noisy verification. In an end-to-end study with real LoRA checkpoints on two base models and three buyer tasks, LLM eBay reduces measured convergence steps by 8.5\% relative to retrieval over self-reported style (95\% CI [1.0\%, 15.9\%]). In model-based market simulations built on a measured style-to-cost relationship, it cuts style-claim error by 96.5\% (from 0.424 to 0.015), recovers 96.3\% of Oracle welfare, saves 72.4\% of the cost of fine-tuning from the base model, and keeps all providers profitable in the default setting.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv eess.SY, nuovi lavori del giorno).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.39347 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:25:30.746Z dalla pagina https://arxiv.org/list/eess.SY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).