LLM-generated Text Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (22 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT
  13 Jun 2024 2 repositories listed
  The increasing prevalence of large language models (LLMs) has significantly advanced text generation, but the human-like quality of LLM outputs presents major challenges in reliably distinguishing between human-authored…
• Red Teaming Language Model Detectors with Language Models
  31 May 2023 2 repositories listed Syntology ran 1 of 18 samples · 17 unverified
  The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
• Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
  1 Apr 2025 1 repository listed
  Short-PHD stabilizes the estimation of the previous PHD method for short texts by inserting off-topic content before the given input text and identifies LLM-generated text based on an established detection threshold.
• Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities
  4 Jan 2025 1 repository listed
  We then design zero-shot statistical tests to (i) distinguish between text generated by two different known sets of LLMs A (non-sanctioned) and B (in-house), and (ii) identify whether text was generated by a known LLM…
• Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection
  16 Dec 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection.
• Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection
  6 Nov 2024 1 repository listed
  Natural Language Generation has been rapidly developing with the advent of large language models (LLMs).
• DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
  31 Oct 2024 1 repository listed Syntology ran 1 of 16 samples · 15 unverified · 16 pointer-only (licence)
  More importantly, we analyzed the potential impact of writing styles, model types, attack methods, the text lengths, and real-world human writing factors on different types of detectors.
• Exploring Naive Approaches to Tell Apart LLMs Productions from Human-written Text
  5 Mar 2024 1 repository listed
  Powerful Large Language Models (large LMs or LLMs) such as BERT and GPT are making the task of detecting machine-generated text more and more prominent and crucial to minimize threats posed by text generation models…
• How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection
  14 Nov 2023 1 repository listed
  Finally, our analysis indicates that the high instruction-following ability of LLMs fosters the large impact of such constraints on detection performance.
• A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
  23 Oct 2023 1 repository listed
  In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research.
• OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
  21 Jul 2023 1 repository listed Syntology ran 6 of 8 samples · 2 unverified
  Experiments in the domain of student essays show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.