Papers by Adaku Uchendu
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark (2023.emnlp-main)
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Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | MULTITuDE benchmarks lack authentic and machine-generated text in languages other than English . defining characteristic of new generation of LLMs is increased quality of text . |
| Approach: | They propose a benchmarking dataset for multilingual machine-generated text detection that compares detectors with authentic and machine-generated texts in 11 languages. |
| Outcome: | The proposed dataset compares detectors with zero-shot and fine-tuned detectors in 11 languages. |
DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects (2026.acl-long)
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| Challenge: | Current disinformation detection systems are predominantly developed and evaluated on Standard American English (SAE) . however, their robustness to dialectal variation is unexplored. |
| Approach: | They propose a benchmark for evaluating disinformation detection robustness across 50 English dialects . they use multi-value's linguistically-grounded transformations to introduce D-CUBE (Dialectal Disinformation Detection Corpus) |
| Outcome: | The proposed model outperforms zero-shot LLMs in human-written dialects while AI-generated content remains stable. |
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis (2023.findings-emnlp)
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| Challenge: | Authorship Analysis is an essential aspect of Natural Language Processing (NLP) for a long time. |
| Approach: | They propose to use 17 human speech datasets and 3 LLMs to create a benchmark for spoken texts. |
| Outcome: | The proposed benchmark encompasses 17 human datasets and AI-generated spoken texts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. |
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection (2025.naacl-long)
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| Challenge: | Recent studies have raised concerns about the potential threats large language models pose to academic integrity and copyright protection. |
| Approach: | They propose a dataset of 46.5K synthetic text pairs that represent three major types of plagiarism: verbatim copying, paraphrasing, and summarization. |
| Outcome: | The proposed dataset shows that GPT-3.5 Turbo can produce high-quality paraphrases and summaries without significantly increasing text complexity compared to GPT-4 Turbo. |
GPT-who: An Information Density-based Machine-Generated Text Detector (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) generate misinformation, memorized content, plagiarized content, toxic speech, and hallucinated content. |
| Approach: | They propose a statistical detector that uses UID to model the unique statistical signature of each LLM and human author for accurate detection. |
| Outcome: | The proposed method outperforms state-of-the-art detectors by over 20% across domains. |
Synthetic Text Detection in the Age of Large Language Models: Watermark vs. Automatic Detection (2026.acl-industry)
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| Challenge: | Large Language Models (LLMs) are ubiquitous and capable of generating long coherent texts that look almost indistinguishable from human-written texts. |
| Approach: | They propose to use watermark and automatic detection to detect synthetic texts generated from Large Language Models (LLMs) they evaluate six different models, six different watermark techniques and two different automatic detectors for different levels of syntactic changes. |
| Outcome: | The proposed methods outperform on unperturbed and perturbed datasets on six different sizes of Qwen2.5 models, six watermark techniques and detectors, and two automatic detectors. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
Authorship Attribution for Neural Text Generation (2020.emnlp-main)
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| Challenge: | Recent advances in deep learning have enabled the generation of realistic artifacts . however, the qualities of texts generated by these models are better, often confusing classifiers if they are not real. |
| Approach: | They propose to use neural network-based language models to generate realistic texts . they investigate the authorship attribution problem in three versions of a text . |
| Outcome: | The proposed models generate texts that are difficult to distinguish from human-written ones . the results show that most generators still generate texts significantly different from human ones compared to other models . |
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation (2023.emnlp-main)
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| Challenge: | Recent ubiquity and disruptive impacts of large language models have raised concerns about their potential to be misused. |
| Approach: | They propose a strategy that leverages LLMs' generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation. |
| Outcome: | The proposed strategy synthesizes authentic and deceptive LLM-generated content through paraphrase-based and perturbation-based prefix-style prompts, respectively. |
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (2024.acl-long)
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Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
| Challenge: | Using a computational approach, we discover that diminishing performance in text classification models is closely associated with the extent of deviation from the original author’s style. |
| Approach: | They propose to use large language models to determine whether a text retains original authorship when it undergoes numerous paraphrasing iterations. |
| Outcome: | The results suggest that authorship should be task-dependent . |
The Shape of Vulnerability: How Adversarial Perturbations Reshape the Topology of Language Model Latent Spaces (2026.acl-srw)
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Angelina Tsai, Shreya Subramanian, Catherine Liu, Kimberly Lopez, Leif Zinn-Brooks, Alexia E. Schulz, Adaku Uchendu
| Challenge: | Large Language Models (LLMs) have unprecedented capabilities, but they pose security concerns . current adversarial attacks exploit vulnerabilities in the embedding space of language models, allowing attackers to bypass safety guardrails and cause significant harmful consequences. |
| Approach: | They propose to use topological data analysis to characterize how adversarial perturbations act on text inputs by computing persistent homology metrics from attention maps across different model architectures. |
| Outcome: | The proposed visualizations show that adversarial perturbations alter higher-dimensional topological features in ways that distinguish them from clean, non-adversarial inputs. |
Beemo: Benchmark of Expert-edited Machine-generated Outputs (2025.naacl-long)
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Ekaterina Artemova, Jason S Lucas, Saranya Venkatraman, Jooyoung Lee, Sergei Tilga, Adaku Uchendu, Vladislav Mikhailov
| Challenge: | Existing benchmarks for machine-generated texts (MGTs) include single-author texts (human-written and machine-generated). |
| Approach: | They propose to benchmark machine-generated outputs (Beemo) which includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases. |
| Outcome: | The proposed benchmark includes 6.5k texts written by humans, generated by ten instruction-finetuned LLMs, and edited by experts for various use cases, ranging from creative writing to summarization. |
Catch Me If You GPT: Tutorial on Deepfake Texts (2024.naacl-tutorials)
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| Challenge: | In recent years, natural language generation (NLG) techniques have advanced, but pose new security risks . this tutorial will be 3 hours long with a mix of lecture and hands-on examples for interactive audience participation. |
| Approach: | They present a tutorial on the security of natural language generation (NLG) they review the latest literature on the detection and obfuscation of deepfake text authorships . |
| Outcome: | This tutorial reviews the latest literature on the detection and obfuscation of deepfake text authorships. |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
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Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | Recent advances in Language Modeling have birthed Large Language Models (LLMs), which exhibit significant improvements, including the ability to generate texts easily misconstrued as humanwritten. |
| Approach: | They compare authorship obfuscation methods against machine-generated text (MGT) in 11 languages and analyze their performance against 37 well-known AO methods. |
| Outcome: | The proposed methods can cause evasion of detection in all languages, with homoglyph attacks particularly successful. |