Using LLMs to Detect LLM-Generated Texts: A Cross-Generation Analysis

arXiv:2609.34691 · cs.CL, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/hyyuan/detect-llm-generated-texts

Terminology

• Accuracy — 57× in this paper · explained in 1 episode
• Hallucination — 6× in this paper · explained in 4 episodes
• Provenance — 6× in this paper · explained in 4 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Explainability — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Know Thyself? On the Incapability and Implications of AI Self-Recognition
• DeepSeek-V3 Technical Report
• Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated
• "I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?
• Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content
• ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text
• Qwen2.5 Technical Report
• Self-Generated Text Recognition: Quality Heuristics, Cross-Task Transfer, and Downstream Bias in LLM Evaluation

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