Show, Don't TELL: Explainable AI-Generated Text Detection. We think this is the way forward to build trust in AI Text Detectors. Happy to discuss and get feedback!</p>\n","updatedAt":"2026-06-02T23:49:26.916Z","author":{"_id":"68fa7c73f382c0374680ad98","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68fa7c73f382c0374680ad98/tPafIo_THzTDrZ98Nc77a.jpeg","fullname":"Suraj Ranganath","name":"suraj-ranganath","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.9463134407997131},"editors":["suraj-ranganath"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/68fa7c73f382c0374680ad98/tPafIo_THzTDrZ98Nc77a.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.27921","authors":[{"_id":"6a1f686ae292c1c78ecb1262","name":"Aldan Creo","hidden":false},{"_id":"6a1f686ae292c1c78ecb1263","name":"Suraj Ranganath","hidden":false}],"publishedAt":"2026-05-27T00:00:00.000Z","submittedOnDailyAt":"2026-06-02T00:00:00.000Z","title":"Show, Don't TELL: Explainable AI-Generated Text Detection","submittedOnDailyBy":{"_id":"68fa7c73f382c0374680ad98","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68fa7c73f382c0374680ad98/tPafIo_THzTDrZ98Nc77a.jpeg","isPro":false,"fullname":"Suraj Ranganath","user":"suraj-ranganath","type":"user","name":"suraj-ranganath"},"summary":"Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distribution performance. However, real-world applicability has stalled because their outputs are misaligned with the needs of users, such as professors, who are presented with a numeric score that has no attached explanation. We tackle this issue with a novel architecture, TELL, that bakes explainability from the ground-up. While our system still offers a numerical score like other detectors for comparability, TELL takes a fundamentally different approach where we aim to show the user the \"tells\" by which the model believes a text is AI or human-written, to empower the user to decide who wrote a text using their own judgment and understanding of the context of the writing and its alleged author. We train TELL on a custom SFT dataset of domain-specific authorship annotations, and further refine the system using GRPO with curriculum learning to improve performance. We achieve competitive performance with state-of-the-art detectors (AUROC 0.927) while natively providing annotations that explain the basis for the detector's decision. We further evaluate the quality of our explanations using a dataset of human annotations and report a high (mean 72.3%) win-rate on annotation concreteness, falsifiability, coherence, plausibility and grounding, allowing users to critically think and decide for themselves. Our work thus reframes the problem of AI-generated text detection in a human-centric perspective and paves the way for a new family of detectors that focus on native explainability.","upvotes":0,"discussionId":"6a1f686ae292c1c78ecb1264","projectPage":"https://ai-tells.tech/","githubRepo":"https://github.com/ACMCMC/TELL","githubRepoAddedBy":"user","ai_summary":"A novel AI-generated text detection system named TELL is introduced that combines high-performance detection with native explainability by showing specific textual indicators that help users make informed judgments about authorship.","ai_keywords":["AI-generated text detection","explainability","TELL architecture","SFT dataset","GRPO","curriculum learning","AUROC","human-centric perspective"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":0},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"]}">
Show, Don't TELL: Explainable AI-Generated Text Detection
Abstract
A novel AI-generated text detection system named TELL is introduced that combines high-performance detection with native explainability by showing specific textual indicators that help users make informed judgments about authorship.
Research on AI-generated text detection has presented a number of approaches to discern human from AI prose, some of which achieving high in-distribution performance. However, real-world applicability has stalled because their outputs are misaligned with the needs of users, such as professors, who are presented with a numeric score that has no attached explanation. We tackle this issue with a novel architecture, TELL, that bakes explainability from the ground-up. While our system still offers a numerical score like other detectors for comparability, TELL takes a fundamentally different approach where we aim to show the user the "tells" by which the model believes a text is AI or human-written, to empower the user to decide who wrote a text using their own judgment and understanding of the context of the writing and its alleged author. We train TELL on a custom SFT dataset of domain-specific authorship annotations, and further refine the system using GRPO with curriculum learning to improve performance. We achieve competitive performance with state-of-the-art detectors (AUROC 0.927) while natively providing annotations that explain the basis for the detector's decision. We further evaluate the quality of our explanations using a dataset of human annotations and report a high (mean 72.3%) win-rate on annotation concreteness, falsifiability, coherence, plausibility and grounding, allowing users to critically think and decide for themselves. Our work thus reframes the problem of AI-generated text detection in a human-centric perspective and paves the way for a new family of detectors that focus on native explainability.
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Show, Don't TELL: Explainable AI-Generated Text Detection. We think this is the way forward to build trust in AI Text Detectors. Happy to discuss and get feedback!
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Cite arxiv.org/abs/2605.27921 in a model README.md to link it from this page.
Cite arxiv.org/abs/2605.27921 in a Space README.md to link it from this page.
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