GaussMark: A Practical Approach for Structural Watermarking of Language Models | Paper Citation Record

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Membership inference attacks from first principles

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This paper cites and Berger, R.

GaussMark: A Practical Approach for Structural Watermarking of Language Models and Berger, R

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This paper cites Scalable watermarking for identifying large language model outputs.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Scalable watermarking for identifying large language model outputs

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A watermark for large language models

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GaussMark: A Practical Approach for Structural Watermarking of Language Models H., Gonzalez, J

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This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

GaussMark: A Practical Approach for Structural Watermarking of Language Models High-dimensional probability: An introduction with applications in data science, volume 47

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This paper cites Watermarking gpt outputs.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermarking gpt outputs

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This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

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GaussMark: A Practical Approach for Structural Watermarking of Language Models GPT-4 Technical Report

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Large Language Models for Mathematical Reasoning: Progresses and Challenges

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This paper cites Mechanistic Interpretability for AI Safety -- A Review.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Mechanistic Interpretability for AI Safety -- A Review

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This paper cites Multi-Bit Distortion-Free Watermarking for Large Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Multi-Bit Distortion-Free Watermarking for Large Language Models

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Membership inference attacks from first principles

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Statistical inference

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This paper cites Watermark Smoothing Attacks against Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermark Smoothing Attacks against Language Models

Reference 22

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This paper cites Undetectable watermarks for language models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Undetectable watermarks for language models

Reference 23

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This paper cites AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

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This paper cites All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text.

GaussMark: A Practical Approach for Structural Watermarking of Language Models All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

Reference 25

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This paper cites Training Verifiers to Solve Math Word Problems.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Training Verifiers to Solve Math Word Problems

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This paper cites Scalable watermarking for identifying large language model outputs.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Scalable watermarking for identifying large language model outputs

Reference 27

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This paper cites Qlora: Efficient finetuning of quantized llms.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Qlora: Efficient finetuning of quantized llms

Reference 28

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GaussMark: A Practical Approach for Structural Watermarking of Language Models The Llama 3 Herd of Models

Reference 29

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 30

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This paper cites Real or fake text?: Investigating human ability to detect boundaries between human-written and machine-generated text.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Real or fake text?: Investigating human ability to detect boundaries between human-written and machine-generated text

Reference 31

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This paper cites ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models

Reference 32

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Publicly-Detectable Watermarking for Language Models

Reference 33

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This paper cites Functional invariants to watermark large transformers.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Functional invariants to watermark large transformers

Reference 34

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GaussMark: A Practical Approach for Structural Watermarking of Language Models On pushing deepfake tweet detection capabilities to the limits

Reference 35

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This paper cites A framework for few-shot language model evaluation, 07 2024.

GaussMark: A Practical Approach for Structural Watermarking of Language Models A framework for few-shot language model evaluation, 07 2024

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GaussMark: A Practical Approach for Structural Watermarking of Language Models GLTR: Statistical Detection and Visualization of Generated Text

Reference 37

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This paper cites Fundamentals of nonparametric Bayesian inference, volume 44.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Fundamentals of nonparametric Bayesian inference, volume 44

Reference 38

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GaussMark: A Practical Approach for Structural Watermarking of Language Models The problem with false positives: Ai detection unfairly accuses scholars of ai plagiarism

Reference 39

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Arcee ' s M erge K it: A toolkit for merging large language models

Reference 40

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Edit Distance Robust Watermarks via Indexing Pseudorandom Codes

Reference 41

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GaussMark: A Practical Approach for Structural Watermarking of Language Models What makes quantization for large language model hard? an empirical study from the lens of perturbation

Reference 42

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A kernel two-sample test

Reference 43

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This paper cites On the Learnability of Watermarks for Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models On the Learnability of Watermarks for Language Models

Reference 44

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GaussMark: A Practical Approach for Structural Watermarking of Language Models LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning

Reference 45

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 46

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Solving Math Word Problems by Combining Language Models With Symbolic Solvers

Reference 47

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GaussMark: A Practical Approach for Structural Watermarking of Language Models SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Reference 48

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GaussMark: A Practical Approach for Structural Watermarking of Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 49

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Towards Optimal Statistical Watermarking

Reference 50

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Editing Models with Task Arithmetic

Reference 51

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GaussMark: A Practical Approach for Structural Watermarking of Language Models MathPrompter: Mathematical Reasoning using Large Language Models

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Automatic Detection of Generated Text is Easiest when Humans are Fooled

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Deep learning for misinformation detection on online social networks: a survey and new perspectives

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Mistral 7B

Reference 55

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A Survey on Large Language Models for Code Generation

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Professors are using chatgpt detector tools to accuse students of cheating

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermark Stealing in Large Language Models

Reference 58

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Scaling Laws for Neural Language Models

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GaussMark: A Practical Approach for Structural Watermarking of Language Models u chemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G \

Reference 60

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A watermark for large language models

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GaussMark: A Practical Approach for Structural Watermarking of Language Models On the Reliability of Watermarks for Large Language Models

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This paper cites She was falsely accused of cheating with ai--and she won’t be the last.

GaussMark: A Practical Approach for Structural Watermarking of Language Models She was falsely accused of cheating with ai--and she won’t be the last

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Observation 917474c1-7ea2-4c81-9777-deaacdd60ff0 · outbound

This paper cites Robust Distortion-free Watermarks for Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Robust Distortion-free Watermarks for Language Models

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This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Gonzalez, Hao Zhang, and Ion Stoica

Reference 65

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This paper cites Waterfall: Scalable framework for robust text watermarking and provenance for llms.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Waterfall: Scalable framework for robust text watermarking and provenance for llms

Reference 66

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This paper cites Asymptotic methods in statistical decision theory.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Asymptotic methods in statistical decision theory

Reference 67

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This paper cites Evaluating Human-Language Model Interaction.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Evaluating Human-Language Model Interaction

Reference 68

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This paper cites Testing statistical hypotheses, volume 3.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Testing statistical hypotheses, volume 3

Reference 69

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This paper cites Watermarking LLMs with Weight Quantization.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermarking LLMs with Weight Quantization

Reference 70

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Hashimoto

Reference 71

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This paper cites Watermarking Techniques for Large Language Models: A Survey.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermarking Techniques for Large Language Models: A Survey

Reference 72

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This paper cites A Semantic Invariant Robust Watermark for Large Language Models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models A Semantic Invariant Robust Watermark for Large Language Models

Reference 73

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This paper cites A survey of text watermarking in the era of large language models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models A survey of text watermarking in the era of large language models

Reference 74

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This paper cites Ai detection tools falsely accuse international students of cheating.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Ai detection tools falsely accuse international students of cheating

Reference 75

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This paper cites A survey on knowledge editing of neural networks.

GaussMark: A Practical Approach for Structural Watermarking of Language Models A survey on knowledge editing of neural networks

Reference 76

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GaussMark: A Practical Approach for Structural Watermarking of Language Models The threat of offensive ai to organizations

Reference 77

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Detectgpt: Zero-shot machine-generated text detection using probability curvature

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:12:51.472233Z

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This paper cites On the problem of the most efficient tests of statistical hypotheses.

GaussMark: A Practical Approach for Structural Watermarking of Language Models On the problem of the most efficient tests of statistical hypotheses

Reference 79

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GaussMark: A Practical Approach for Structural Watermarking of Language Models New ai classifier for indicating ai-written text

Reference 80

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This paper cites MarkLLM: An Open-Source Toolkit for LLM Watermarking.

GaussMark: A Practical Approach for Structural Watermarking of Language Models MarkLLM: An Open-Source Toolkit for LLM Watermarking

Reference 81

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This paper cites No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices.

GaussMark: A Practical Approach for Structural Watermarking of Language Models No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

Reference 82

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Pytorch: An imperative style, high-performance deep learning library

Reference 83

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 84

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Revisiting the Robustness of Watermarking to Paraphrasing Attacks

Reference 85

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Can AI-Generated Text be Reliably Detected?

Reference 86

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Chatgpt: Optimizing language models for dialogue

Reference 87

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This paper cites Automatic fake news detection with pre-trained transformer models.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Automatic fake news detection with pre-trained transformer models

Reference 88

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This paper cites The truth is in there: Improving reasoning in language models with layer-selective rank reduction.

GaussMark: A Practical Approach for Structural Watermarking of Language Models The truth is in there: Improving reasoning in language models with layer-selective rank reduction

Reference 89

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 90

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This paper cites Gemma: Open Models Based on Gemini Research and Technology.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 91

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Daniel Freeman, Theodore R

Reference 92

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Parents sue son’s high school history teacher over ai ‘cheating’ punishment, October 2024

Reference 93

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Democratizing Neural Machine Translation with OPUS-MT

Reference 94

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GaussMark: A Practical Approach for Structural Watermarking of Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 95

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GaussMark: A Practical Approach for Structural Watermarking of Language Models A professor accused his class of using chatgpt, putting diplomas in jeopardy

Reference 96

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GaussMark: A Practical Approach for Structural Watermarking of Language Models High-dimensional probability: An introduction with applications in data science, volume 47

Reference 97

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Superglue: A stickier benchmark for general-purpose language understanding systems

Reference 98

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GaussMark: A Practical Approach for Structural Watermarking of Language Models MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning

Reference 99

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GaussMark: A Practical Approach for Structural Watermarking of Language Models MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

Reference 100

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GaussMark: A Practical Approach for Structural Watermarking of Language Models Unresolved cited work

Reference 101

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This paper cites Huggingface's transformers: State-of-the-art natural language processing.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Huggingface's transformers: State-of-the-art natural language processing

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:12:50.785608Z

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Observation 49d1575c-c4a2-4b66-9770-cdd25e989344 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 103

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This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 104

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