WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2311.07138v2)

Abstract: To mitigate the potential misuse of LLMs, recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible trace for watermark detection. Due to the two-stage nature of the task, most studies evaluate the generation and detection separately, thereby presenting a challenge in unbiased, thorough, and applicable evaluations. In this paper, we introduce WaterBench, the first comprehensive benchmark for LLM watermarks, in which we design three crucial factors: (1) For benchmarking procedure, to ensure an apples-to-apples comparison, we first adjust each watermarking method's hyper-parameter to reach the same watermarking strength, then jointly evaluate their generation and detection performance. (2) For task selection, we diversify the input and output length to form a five-category taxonomy, covering $9$ tasks. (3) For evaluation metric, we adopt the GPT4-Judge for automatically evaluating the decline of instruction-following abilities after watermarking. We evaluate $4$ open-source watermarks on $2$ LLMs under $2$ watermarking strengths and observe the common struggles for current methods on maintaining the generation quality. The code and data are available at https://github.com/THU-KEG/WaterBench.

An Overview of "WaterBench: Towards Holistic Evaluation of Watermarks for LLMs"

The paper "WaterBench: Towards Holistic Evaluation of Watermarks for LLMs" introduces a comprehensive framework for assessing watermarking schemes in LLMs. The purpose of watermarking these models is to mitigate the risk of misuse, such as generating fake news, by embedding detectable traces into text generated by LLMs. Previous assessments of watermarking methods have fallen short due to the two-stage nature of watermarking tasks, evaluating the generation and detection as separate processes without integrating these aspects into a single, cohesive evaluation scheme. The WaterBench benchmark aims to address these shortcomings by offering a unified and impartial evaluation of watermarking methodologies.

Core Components of WaterBench

WaterBench is introduced as the first benchmark designed to evaluate both the generation and detection performance of watermarked LLMs. The paper outlines the three key components of this benchmark:

1. Benchmarking Procedure: To ensure consistency and fairness, WaterBench normalizes the watermarking strength of different methods by adjusting the hyperparameters to a matching level before assessing both the generation and detection processes together. This method avoids discrepancies stemming from separate evaluations.
2. Task Selection: WaterBench categorizes tasks based on input and output length, forming a five-category taxonomy covering nine distinct tasks. This includes both predefined closed tasks (e.g., code completion, factual question answering) and open-ended tasks (such as instruction following), offering a structured analysis across various conditions that might affect watermark visibility and detection difficulty.
3. Evaluation Metric: The benchmark introduces a novel evaluation metric by employing GPT4-Judge, an AI-based assessment tool, for evaluating the functional decline in the instructional-following ability of LLM post-watermarking. This measure aims to capture quality degradation in the text generation phase that might arise due to watermark insertion.

Evaluation and Findings

The paper evaluates four open-source watermarking methods on two popular LLMs — Llama2-chat and InternLM — across two watermarking strengths. Findings indicate that the detection performance varies remarkably with the robustness of the watermark, specifically noting challenges in shorter answer tasks where true positive rates decline. It highlights that without aligning watermarking strengths, comparative assessments could unjustly favor one watermark over another. Even with unified strengths, there remains a generally observable decline in text generation quality upon watermarking, where certain watermarking methods exhibit notable decreases in generation performance on specific open-ended tasks.

Additionally, the enforcement of watermarking strength sheds light on the delicate balancing act between ensuring watermark robustness and preserving generation quality. Such insights underscore the paper's recognition that current methodologies for embedding detectable watermarks tend to struggle with maintaining the output fidelity of LLM-generated texts.

Implications and Future Directions

The WaterBench benchmark holds significant promise for improving the rigor and breadth of future watermark evaluations for LLMs. It sets a precedent for conducting integrated assessments that can offer more meaningful comparisons between different watermarking techniques. Practically, its contributions highlight the pressing need for developers to consider generation quality when creating watermarking algorithms to ensure seamless integration that neither compromises utility nor performance of the original models.

Theoretical implications anchor around reinforcing model alignment in the presence of potential misuse avenues, where WaterBench's methodologies and findings illuminate potential paths for refined watermarking that aligns effective detection with minimal degradation. Future research might explore adaptive watermarking strengths and investigate how dynamically modulating watermark robustness responsive to task-specific requirements can offer more balanced trade-offs. Moreover, the exploration of zero-shot or transfer-learning-based evaluations with the WaterBench framework can expand its utility across an extensive range of unseen tasks and model architectures.

In conclusion, WaterBench represents a critical academic contribution towards harmonizing the disparate and often misaligned methods for watermark evaluation in LLMs, presenting a holistic, adaptive framework that underscores both the nuances and necessities for advancing watermark research.