Papers with plagiarism detection
CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis (2025.findings-naacl)
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| Challenge: | Existing studies on LLM-LLM collaboration for open-ended tasks have focused on human-LLm interaction. |
| Approach: | They propose to generate a dataset exclusively for LLMs to explore multi-LLM collaboration scenarios . they extend their authorship-related tasks for multi-llm settings and extend their baselines . |
| Outcome: | The authors extend authorship-related tasks for multi-LLM settings and present baselines for LLM-LLMS collaboration. |
Adversarial Math Word Problem Generation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have revolutionized the educational landscape due to the great improvements in their natural language generation and problem-solving capabilities. |
| Approach: | They propose a cost-effective approach to attack large language models using abstract syntax trees to generate adversarial examples that preserve the structure and difficulty of the original questions aimed for assessment. |
| Outcome: | The proposed approach significantly degrades students' math problem-solving ability on open- and closed-source LLMs. |
Semantic Relatedness Based Re-ranker for Text Spotting (D19-1)
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| Challenge: | Existing approaches to text spotting are limited by semantic similarity, but they can be useful for other tasks. |
| Approach: | They propose a neural approach to learn semantic relatedness from existing sentences. |
| Outcome: | The proposed approach outperforms existing approaches when applied to a text spotting task. |
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. |
Multilevel Text Alignment with Cross-Document Attention (2020.emnlp-main)
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| Challenge: | Existing alignment methods operate at a single, predefined level and cannot learn to align texts at sentence and document levels. |
| Approach: | They propose a learning approach that equips hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels. |
| Outcome: | The proposed model outperforms existing hierarchical, attention encoders on citation recommendation and plagiarism detection tasks. |
GNAT: A General Narrative Alignment Tool (2023.emnlp-main)
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| Challenge: | Algorithmic sequence alignment is a common operation in many NLP tasks, but it is difficult to recognize similarities between distant versions of narratives such as translations and retellings. |
| Approach: | They propose a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics. |
| Outcome: | The proposed approach can be used to identify similarities between two different versions of narratives, and to define rigorous p-values on the significance of any alignment. |
Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification (2023.findings-emnlp)
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| Challenge: | Existing AV techniques, including stylometric and deep learning, face limitations in terms of data requirements and lack of explainability. |
| Approach: | They propose a technique that leverages Large-Language Models (LLMs) to provide step-by-step stylometric explanation prompts to verify authorship. |
| Outcome: | The proposed technique outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations. |
Monolingual Paraphrase Detection Corpus for Low Resource Pashto Language at Sentence Level (2024.lrec-main)
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| Challenge: | Existing research on sentence-level paraphrase detection in Pashto has focused on English, but no work has been done on low-resource Pashtone. |
| Approach: | They propose to annotate sentences in Pashto to detect paraphrases . they will publicize a subset of 1,800 instances from their corpus, free from licensing issues. |
| Outcome: | The proposed corpus contains 6,727 sentences, encompassing 3,687 paraphrased and 3,040 non-paraphrased sentences. |
The Million Authors Corpus: A Cross-Lingual and Cross-Domain Wikipedia Dataset for Authorship Verification (2025.findings-acl)
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| Challenge: | Authorship verification (AV) is a crucial task for identity verification, accountlinking, historical linguistics, and AI-generated text identification. |
| Approach: | They propose to use Wikipedia's Million Authors Corpus to examine authorship verification models on a broad scale. |
| Outcome: | The proposed dataset includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. |
How to Generalize the Detection of AI-Generated Text: Confounding Neurons (2025.findings-emnlp)
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| Challenge: | Linguistic and domain confounders introduce spurious correlations, leading to poor out-of-distribution (OOD) performance. |
| Approach: | They propose a novel post-hoc, neuron-level intervention framework to disentangle AI-generated text detection factors from data-specific biases. |
| Outcome: | The proposed framework reduces topic-specific biases by encoding individual neurons within transformers-based detectors rather than task-specific signals. |