Papers with LSH
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024.findings-acl)
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| Challenge: | Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. |
| Approach: | They propose a watermark which assigns signatures to each watermarked sentence according to locality-sensitive hashing (LSH) they propose k-SemStamp, which uses kmeans clustering to partition the semantic space with awareness of inherent semantic structure. |
| Outcome: | The proposed watermark improves its robustness and sampling efficiency while preserving the generation quality, making it more effective for machine-generated text detection. |
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024.naacl-long)
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Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
| Challenge: | Existing watermarked generation algorithms employ token-level designs and are vulnerable to paraphrase attacks. |
| Approach: | They propose a sentence-level watermarking algorithm that uses locality-sensitive hashing to partition the semantic space of sentences. |
| Outcome: | The proposed algorithm is more robust than the existing state-of-the-art method on paraphrasers and domains, while posing only minor degradations to SemStamp. |
Transferable Neural Projection Representations (N19-1)
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| Challenge: | Neural word embeddings require lookup and a large memory footprint making it hard to deploy on-device. |
| Approach: | They propose a skip-gram based architecture coupled with Locality-Sensitive Hashing projections to learn efficient dynamically computable representations. |
| Outcome: | The proposed model performs better than previous models on multiple NLP tasks. |
A Strong Baseline for Query Efficient Attacks in a Black Box Setting (2021.emnlp-main)
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| Challenge: | Existing black box search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. |
| Approach: | They propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks. |
| Outcome: | The proposed attack reduces query count by 75% across all datasets and target models compared to prior attacks in a limited query setting. |