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Building Auditable AI Text Detectors with Sparse Autoencoders

Paper recorded by Signals 4 on 2026-10-21 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CL · 自然语言处理 · first seen 2026-10-22

Abstract

AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide

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#180 most recent of 660 cs.CL papers we have recorded · ↑ newer: GoiBERT: Compact Vietnamese Language Models via Two-Stage Knowledge Di · ↓ older: HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hi
Cite this page: Building Auditable AI Text Detectors with Sparse Autoencoders: the #180 most recent of 660 cs.CL papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/building-auditable-ai-text-detectors-with-sparse-autoencoders.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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