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proxy training data

Proxy training data refers to substitute or surrogate datasets used to train a machine learning model when direct access to the target operational data is restricted, scarce, or unavailable. By sharing structural, semantic, or statistical similarities with the target distribution, this stand-in data allows a model to learn relevant representations and baseline patterns. Practitioners frequently leverage proxy data derived from related domains, accessible public repositories, or synthetic generation pipelines to build or pretrain systems, which can subsequently be adapted or fine-tuned to the true target task using only minimal in-domain annotations.

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Cross-Domain Detection of GPT-2-Generated Technical Text

Cross-Domain Detection of GPT-2-Generated Technical Text

Juan Diego Rodriguez, Todd Hay, David Gros, Zain Shamsi, Ravi Srinivasan

OrganizationsEn SoluciónUniversity of California, DavisUniversity of Texas at Austin

Why you should read this

Demonstrates that machine-generated technical text detectors can successfully transfer across distinct scientific domains with only a few hundred labeled examples, enabling effective identification of synthetic content and paragraph tampering in full-length research papers.

Machine-generated text presents a potential threat not only to the public sphere, but also to the scientific enterprise, whereby genuine research is undermined by convincing, synthetic text. In this paper we examine the problem of detecting GPT-2-generated technical research text. We first consider the realistic scenario where the defender does not have full information about the adversary's text generation pipeline, but is able to label small amounts of in-domain genuine and synthetic text in order to adapt to the target distribution. Even in the extreme scenario of adapting a physics-domain detector to a biomedical detector, we find that only a few hundred labels are sufficient for good performance. Finally, we show that paragraph-level detectors can be used to detect the tampering of full-length documents under a variety of threat models.

Added

2026-09-26