# ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination arXiv: https://arxiv.org/abs/2409.14285 ยท HTML: https://mlsanity.com/paper/2409.14285/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 46 of its 69 references that are in the map. - First posted on arXiv 22 Sep 2024. ## Builds on (most cited first, 30 of 46) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023) - [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018) - [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024) - [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023) - [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023) - [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024) - [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019) - [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019) - [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023) - [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023) - [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021) - [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020) - [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019) - [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024) - [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023) - [On the Reliability of Watermarks for Large Language Models](https://mlsanity.com/paper/2306.04634/) (2023) - [LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples](https://mlsanity.com/paper/2310.01469/) (2023) - [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024) - [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017) - [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021) - [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019) - [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023) - [Watermark Stealing in Large Language Models](https://mlsanity.com/paper/2402.19361/) (2024) - [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://mlsanity.com/paper/2107.01294/) (2021) - [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023) - [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024) - [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022) - [LLMDet: A Third Party Large Language Models Generated Text Detection Tool](https://mlsanity.com/paper/2305.15004/) (2023) - [Large Language Models can be Guided to Evade AI-Generated Text Detection](https://mlsanity.com/paper/2305.10847/) (2023) ## Built on by (most cited first, 6 of 6) - [DAMAGE: Detecting Adversarially Modified AI Generated Text](https://mlsanity.com/paper/2501.03437/) (2025) - [Beyond Easy Wins: A Text Hardness-Aware Benchmark for LLM-generated Text Detection](https://mlsanity.com/paper/2507.15286/) (2025) - [CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models](https://mlsanity.com/paper/2510.02342/) (2025) - [LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text](https://mlsanity.com/paper/2509.21269/) (2025) - [Show, Don't TELL: Explainable AI-Generated Text Detection](https://mlsanity.com/paper/2605.27921/) (2026) - [Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution](https://mlsanity.com/paper/2607.14905/) (2026) ## Abstract (arXiv) While large language models (LLMs) exhibit significant utility across various domains, they simultaneously are susceptible to exploitation for unethical purposes, including academic misconduct and dissemination of misinformation. Consequently, AI-generated text detection systems have emerged as a countermeasure. However, these detection mechanisms demonstrate vulnerability to evasion techniques and lack robustness against textual manipulations. This paper introduces back-translation as a novel technique for evading detection, underscoring the need to enhance the robustness of current detection systems. The proposed method involves translating AI-generated text through multiple languages before back-translating to English. We present a model that combines these back-translated texts to produce a manipulated version of the original AI-generated text. Our findings demonstrate that the manipulated text retains the original semantics while significantly reducing the true positive rate (TPR) of existing detection methods. We evaluate this technique on nine AI detectors, including six open-source and three proprietary systems, revealing their susceptibility to back-translation manipulation. In response to the identified shortcomings of existing AI text detectors, we present a countermeasure to improve the robustness against this form of manipulation. Our results indicate that the TPR of the proposed method declines by only 1.85% after back-translation manipulation. Furthermore, we build a large dataset of 720k texts using eight different LLMs. Our dataset contains both human-authored and LLM-generated texts in various domains and writing styles to assess the performance of our method and existing detectors. This dataset is publicly shared for the benefit of the research community.