Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2410.14259v1)

Abstract: The rapid development of LLMs, like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse. While detecting LLM-generated content is crucial for mitigating these risks, current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content. This approach introduces two novel tasks: LLM Role Recognition (LLM-RR), a multi-class classification task that identifies specific roles of LLM in content generation, and LLM Influence Measurement (LLM-IM), a regression task that quantifies the extent of LLM involvement in content creation. To support these tasks, we propose LLMDetect, a benchmark designed to evaluate detectors' performance on these new tasks. LLMDetect includes the Hybrid News Detection Corpus (HNDC) for training detectors, as well as DetectEval, a comprehensive evaluation suite that considers five distinct cross-context variations and multi-intensity variations within the same LLM role. This allows for a thorough assessment of detectors' generalization and robustness across diverse contexts. Our empirical validation of 10 baseline detection methods demonstrates that fine-tuned PLM-based models consistently outperform others on both tasks, while advanced LLMs face challenges in accurately detecting their own generated content. Our experimental results and analysis offer insights for developing more effective detection models for LLM-generated content. This research enhances the understanding of LLM-generated content and establishes a foundation for more nuanced detection methodologies.

Beyond Binary: Fine-Grained Detection of LLM-Generated Text

The research paper titled "Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement" addresses the challenge of detecting content generated by LLMs in collaborative human-AI settings. The rapid evolution of LLMs, such as ChatGPT, has resulted in an increased presence of automatically generated content on online platforms. This raises concerns regarding misinformation, bias, and privacy, necessitating robust detection mechanisms. The authors propose moving beyond binary classification, which merely distinguishes between human and AI-generated content, to a more nuanced approach that identifies the role and extent of LLM involvement.

Proposed Paradigm and Tasks

To address the complexities of real-world use cases where humans and LLMs work together, the authors introduce two core tasks:

• LLM Role Recognition (LLM-RR): A multi-class classification task designed to determine the role of an LLM in generating content. This task seeks to classify content based on whether it is entirely human-authored, fully LLM-generated, or produced with varying degrees of human-LLM collaboration, such as editing or content extension.
• LLM Influence Measurement (LLM-IM): A regression task aimed at quantifying the extent of LLM involvement or influence in the final text. This task offers a continuous metric, the LLM Involvement Ratio (LIR), ranging from 0 (entirely human-authored) to 1 (entirely LLM-generated), facilitating nuanced evaluations of content collaboration.

Benchmark and Empirical Evaluation

To evaluate these tasks, the authors present LLMDetect, a benchmark that comprises the Hybrid News Detection Corpus (HNDC) and DetectEval. The HNDC provides a diverse collection of news articles with varying LLM involvement roles, while DetectEval contains a comprehensive suite for real-world evaluation, covering cross-context scenarios.

Upon empirical validation of several baseline detection models, it was observed that fine-tuned Pre-trained LLMs (PLMs) surpassed alternate methods. PLM-based models, especially Longformer and DeBERTa, demonstrated superior performance in role recognition and involvement measurement, indicating their robustness and adaptability to different content scenarios. In contrast, advanced LLMs presented difficulties in recognizing their own generated content, underperforming in comparison to PLM-based detectors.

Implications and Future Directions

The research makes pivotal contributions by enhancing the granularity of LLM-generated text detection. It introduces novel methodologies that can uncover deeper relationships between human and AI contributions, which are vital for ensuring content integrity and trust on digital platforms.

Future developments could enhance the applicability of these detection models to various domains beyond news articles, such as academic writing or creative content. Moreover, integrating these models into existing content moderation tools could help mitigate risks associated with the pervasive use of AI tools in content generation.

In conclusion, the paper establishes a robust framework for understanding and detecting complex human-LLM collaborations in content creation. It lays the groundwork for further innovations in AI-generated content detection, which is increasingly critical in our digitally interconnected world.