Papers with SimLLM
Unsupervised Detection of LLM-Generated Text in Korean Using Syntactic and Semantic Cues (2026.findings-eacl)
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| Challenge: | Prior work focused on English, leaving low-resource languages such as Korean underexplored. |
| Approach: | They propose an unsupervised framework that integrates syntactic token cohesiveness and semantic regeneration similarity to detect Korean text. |
| Outcome: | The proposed framework outperforms baselines in Korean and other low-resource languages without training. |
SimLLM: Detecting Sentences Generated by Large Language Models Using Similarity between the Generation and its Re-generation (2024.emnlp-main)
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| Challenge: | Prior studies have detected the generation of non-analogous text with substantial differences between original and generated content. |
| Approach: | They propose a method to detect analogous machine-generated sentences that closely mimic human-written ones by estimating the similarity between an input sentence and its generated counterpart. |
| Outcome: | The proposed approach outperforms existing methods in academic dishonesty, spam dissemination, and misinformation propagation. |