In-Context Watermarks for Large Language Models (2505.16934v1)

Abstract: The growing use of LLMs for sensitive applications has highlighted the need for effective watermarking techniques to ensure the provenance and accountability of AI-generated text. However, most existing watermarking methods require access to the decoding process, limiting their applicability in real-world settings. One illustrative example is the use of LLMs by dishonest reviewers in the context of academic peer review, where conference organizers have no access to the model used but still need to detect AI-generated reviews. Motivated by this gap, we introduce In-Context Watermarking (ICW), which embeds watermarks into generated text solely through prompt engineering, leveraging LLMs' in-context learning and instruction-following abilities. We investigate four ICW strategies at different levels of granularity, each paired with a tailored detection method. We further examine the Indirect Prompt Injection (IPI) setting as a specific case study, in which watermarking is covertly triggered by modifying input documents such as academic manuscripts. Our experiments validate the feasibility of ICW as a model-agnostic, practical watermarking approach. Moreover, our findings suggest that as LLMs become more capable, ICW offers a promising direction for scalable and accessible content attribution.

In-Context Watermarks for LLMs: An Analytical Overview

The paper "In-Context Watermarks for LLMs" presents a novel approach to watermarking text generated by LLMs without requiring access to the decoding process. This work addresses a significant challenge faced in real-world scenarios where model owners or users deploying AI-based applications do not have direct control over LLM operations, thus limiting the applicability of conventional watermarking techniques that rely on alterations within the model's internal mechanics.

Core Contributions and Methodological Insight

The authors introduce and systematically evaluate In-Context Watermarking (ICW), a method leveraging the innate capabilities of LLMs in instruction-following and in-context learning to embed watermarks directly through prompt engineering. The essence of ICW lies in incorporating watermark instructions within user queries, invoking LLMs to produce outputs that inherently carry a detectable watermark. Through ICW, the authors propose a model-agnostic and practical solution enabling reliable attribution of AI-generated content applicable in sensitive contexts such as academic peer review detection.

The paper classifies four ICW strategies based on granularity: Unicode, Initials, Lexical, and Acrostics, each employing a distinct approach to incorporate watermarks:

• Unicode ICW utilizes invisible characters to signal AI intervention while keeping textual fidelity intact.
• Initials ICW biases word selection, encouraging words beginning with specific letters.
• Lexical ICW asks the model to prefer certain green-listed words, testing the model's contextual adaptation and vocabulary use.
• Acrostics ICW constructs sentences such that the first letter of each sentence aligns with a predetermined sequence.

The ICWs are assessed on their detectability, robustness to text modifications, and impact on text quality, offering extensive benchmarks against contemporary techniques.

Experimental Findings

The paper reports empirical results using advanced LLMs, demonstrating that ICWs achieve strong performance metrics across detection sensitivity and robustness, akin to or exceeding current post-hoc watermarking strategies. ICWs exhibit varied resilience against attacks such as random editing, paraphrasing, and synonym replacements. Notably, sophisticated ICWs like Lexical and Acrostics outperform simpler Unicode methods under robustness trials, achieving superior detection rates while maintaining high text quality.

Moreover, initial trials in indirect prompt injection settings outline potential real-world applications, marking ICW as practically viable for identifying AI misuse in academic reviews when reviewers inadvertently incorporate covert instructions present in uploaded manuscripts.

Theoretical Implications and Future Directions

This research contributes to the theoretical understanding of AI-generated text provenance by establishing ICW as a scalable, model-independent technique adaptable to multiple deployment contexts. It suggests that as LLMs continue to advance in their instruction-following capabilities, ICW can become more intricate and potent, fulfilling enhanced content tracking and authenticity assurance needs without necessitating intervention at the model level.

The exploration of ICW opens avenues for future research to explore optimizing prompt engineering approaches to improve watermark command follow-through, potentially crafting alignment tasks to integrate ICW within model training phases. Consideration of ICW as an alignment-oriented feature that could be embedded within LLM behavior represents a promising frontier in maintaining textual fidelity while ensuring reliable source attribution.

Concluding Remarks

In summary, the paper effectively demonstrates that In-Context Watermarking can serve as a robust and versatile mechanism to attribute AI-generated text in environments where traditional watermarking methods are impractical. Its implications reinforce the increasing pertinence of surveillance and transparency in AI applications, addressing ethical considerations and the technical dynamics of digital content dissemination. ICW marks a consequential stride towards reinforcing accountability and establishing trust in AI-generated outputs, echoing broader calls for transparency and integrity within the machine learning community.