ReMoDetect: Reward Models Recognize Aligned LLM's Generations

Abstract: The remarkable capabilities and easy accessibility of LLMs have significantly increased societal risks (e.g., fake news generation), necessitating the development of LLM-generated text (LGT) detection methods for safe usage. However, detecting LGTs is challenging due to the vast number of LLMs, making it impractical to account for each LLM individually; hence, it is crucial to identify the common characteristics shared by these models. In this paper, we draw attention to a common feature of recent powerful LLMs, namely the alignment training, i.e., training LLMs to generate human-preferable texts. Our key finding is that as these aligned LLMs are trained to maximize the human preferences, they generate texts with higher estimated preferences even than human-written texts; thus, such texts are easily detected by using the reward model (i.e., an LLM trained to model human preference distribution). Based on this finding, we propose two training schemes to further improve the detection ability of the reward model, namely (i) continual preference fine-tuning to make the reward model prefer aligned LGTs even further and (ii) reward modeling of Human/LLM mixed texts (a rephrased texts from human-written texts using aligned LLMs), which serves as a median preference text corpus between LGTs and human-written texts to learn the decision boundary better. We provide an extensive evaluation by considering six text domains across twelve aligned LLMs, where our method demonstrates state-of-the-art results. Code is available at https://github.com/hyunseoklee-ai/ReMoDetect.

• The paper introduces a novel detection framework based on reward models to distinguish aligned LLM-generated texts from human-written content.
• It details continual fine-tuning and mixed human/LLM training schemes that enhance the model’s ability to amplify preference score differences.
• Empirical tests show robust performance with high AUROC scores across multiple LLMs and challenging text scenarios, ensuring scalability and adaptability.

ReMoDetect: Enhancing Detection of Aligned LLM Generations

The paper "ReMoDetect: Reward Models" presents a novel approach to detecting text generated by LLMs that have undergone alignment training. The rapid advancements in LLMs have introduced significant societal concerns due to their potential misuse in generating fake news, causing ethical dilemmas, and other malevolent activities. A primary challenge in counteracting these issues is the detection of LLM-generated texts (LGTs), which have grown in sophistication due to alignment training aimed at enhancing their preference for human-like text generation. This paper provides a coherent methodology to exploit alignment characteristics, offering a detection framework called ReMoDetect, which marks a significant enhancement over existing strategies.

Methodological Contribution

Unlike traditional binary classifiers that may suffer from biases due to training on specific LGTs, ReMoDetect capitalizes on using reward models conceptually viewed as surrogates for human preferences. The core idea is based on an insightful observation that aligned LLMs tend to generate texts that possess even higher predicted preference scores than those written by humans. This characteristic originates from their alignment training, which optimizes them to produce text that resonates more strongly with human preferences.

The paper expounds two novel training schemes to sharpen the detection capability of these reward models:

1. Continual Preference Fine-Tuning: This involves further fine-tuning the reward model through continual learning to amplify the preference scores differentiating LGTs from human-written texts. Implementing a replay buffer mitigates potential model overfitting, maintaining generalization across unseen domains.
2. Human/LLM Mixed Texts: This method creates a dataset of mixed texts, partially rephrased using aligned LLMs. Such texts serve as a bridge between purely machine-generated and human-written texts, refining the decision boundary of the reward model.

Empirical Evaluation

Subsequent empirical evaluations in the paper underscore the framework’s efficacy, showcasing superior performance across several domains and multiple state-of-the-art LLMs, including GPT-4, Llama3, and Claude. ReMoDetect is tested on tasks from diverse datasets like Fast-DetectGPT and MGTBench, consistently outperforming other methods, such as DetectGPT and Fast-DetectGPT, in AUROC benchmarks. Notably, the robust performance of ReMoDetect extends to various challenging scenarios, such as detecting rephrased LGTs and shorter text lengths.

Moreover, the proposed methodology demonstrates robust generalization capabilities. By using a singular reward model across tests involving different LLMs and domains not encountered during training, ReMoDetect maintains high detection accuracy, highlighting the scalability and adaptability of the approach.

Implications and Future Directions

The introduction of ReMoDetect holds substantial potential implications for both the theoretical landscape of AI alignment and practical applications in NLP. This methodology not only provides a tool to identify LGTs with high precision but also delineates a pathway for leveraging the inherent structures introduced through alignment training.

Future research directions could explore scaling ReMoDetect with larger reward models to potentially enhance detection performance further. The extension of such frameworks for improving LLM alignment techniques themselves also emerges as a sensible consideration, aiming to create models that generate more human-like and ethically aligned responses even under adversarial settings.

In conclusion, the ReMoDetect framework presents a sophisticated approach that addresses the emergent need for detecting advanced LLM-generated texts. Its reliance on the distinct properties of alignment-trained models and the strategic use of reward models underscores the evolving interplay between model design and ethical oversight in NLP technologies.