GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models generate human-like content, but they also pose a problem with generation diversity, negatively impacting generation diversity and user experience. |
| Approach: | They propose a Logits-Addition watermark and three variants that aim to enhance diversity to overcome generation diversity challenges. |
| Outcome: | The Logits-Addition watermark outperforms the Logits+Trick-based watermark in diversity tests and outperformed other decoding-based methods by 0.1 to 0.3. |
Similar Papers
Subtle Signatures, Strong Shields: Advancing Robust and Imperceptible Watermarking in Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have led to an increase in AI-generated text on the Internet, presenting a crucial challenge to differentiate AI-created content from human-written text. |
| Approach: | They propose a novel approach to embed watermarks into LLMs that leverages token prior probabilities to improve detectability and maintain watermark imperceptibility. |
| Outcome: | The proposed method improves detectability and imperceptibility of watermarks by partitioning tokens into two distinct groups based on prior probabilities and employing tailored strategies for each group. |
SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results demonstrate that SimMark surpasses previous sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains. |
| Approach: | They propose a sentence-level watermarking algorithm that makes LLM outputs traceable without requiring access to model internals. |
| Outcome: | The proposed algorithm surpasses previous sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains while maintaining the text quality and fluency. |
From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing watermarking methods face limitations that hinder their effectiveness in diverse and adversarial scenarios. |
| Approach: | They propose a symbiotic watermarking framework with three strategies: serial, parallel, and hybrid. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art (SOTA) performance. |
XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for embedding binary messages into LLM-generated text suffer from key limitations, such as a poor trade-off between text quality and decoding accuracy. |
| Approach: | They propose a method for embedding binary messages into Large Language Model (LLM)-generated text that uses a limited number of tokens to decode and recover the encoded message. |
| Outcome: | The proposed method significantly outperforms existing methods in multiple downstream tasks and will be made publicly available upon acceptance. |
Ensemble Watermarks for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing watermarks for large language models lack flexibility and struggle with attacks such as paraphrasing. |
| Approach: | They propose a multi-feature method for generating watermarks that combines multiple distinct watermark features into an ensemble watermark. |
| Outcome: | The proposed method achieves 98% detection rate after paraphrasing attack and 49% after paraphrase attack. |
Improved Unbiased Watermark for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Unbiased watermarks allow to distinguish between text generated by humans and machines without causing distortion. |
| Approach: | They introduce a family of unbiased, Multi-Channel-based watermarks that partition the language model into segments and promote token probabilities within a selected segment based on a watermark key. |
| Outcome: | The proposed watermarks preserve the original distribution of the language model and offer significant improvements in detectability and robustness over existing unbiased watermark systems. |
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection. |
| Approach: | They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance. |
| Outcome: | The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality. |
PostMark: A Robust Blackbox Watermark for Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to detect LLM-generated text require access to the underlying LLM’s logits, which LLM providers are loath to share due to fears of model distillation. |
| Approach: | They develop a post-hoc watermarking procedure that inserts an input-dependent set of words into the text after the decoding process has completed. |
| Outcome: | The proposed method is more robust to paraphrasing attacks than existing methods. |
Synthetic Text Detection in the Age of Large Language Models: Watermark vs. Automatic Detection (2026.acl-industry)
Copied to clipboard
| 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. |
Watermarking Large Language Models: An Unbiased and Low-risk Method (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have highlighted the risk of misusing them, raising the need for accurate detection of LLM-generated content. |
| Approach: | They propose a method to inject imperceptible identifiers into large language models (LLMs) this method is unbiased and preserves the original token distribution in expectation . |
| Outcome: | The proposed method preserves the original token distribution in expectation and has lower risk of producing unsatisfactory outputs in low-entropy scenarios compared to existing unbiased watermarks. |