Objective

The research aimed to evaluate the quality of datasets used for detecting AI-generated content, questioning the reliability of existing detectors which often achieve high benchmark scores that may be due to low-quality evaluation datasets.

Method

The study conducted a systematic review of datasets from competitions focused on detecting AI-generated texts. It proposed methods to assess dataset quality through linguistic features, robustness to perturbations, and analysis of attention patterns in language models.

Results

The analysis revealed that existing datasets tend to have significant variances in quality; for instance, some datasets showed near-perfect detection performance in controlled settings but struggled with real-world text due to structural biases and low-quality generative outputs.

Significance

The findings highlighted the inadequate quality of many datasets used in the detection of AI-generated texts, recommending the development of comprehensive quality assessment systems for datasets to enhance detector frameworks and maintain information integrity in automated contexts.

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Objective

The research seeks to address the limitations of existing preference alignment in large language models (LLMs) that uniformly block access to non-preferred knowledge, which could be beneficial for advanced users.

Method

The authors propose a framework called SudoLM that implements authorization alignment, allowing LLMs to control access to parametric knowledge based on user credentials. Authorized users can use a SUDO key to unlock restricted knowledge, while non-qualified users are denied access. The study employs a dataset constructed from public and privileged knowledge samples, utilizing statistical methods for data analysis and fine-tuning the model to minimize cross-entropy loss during training.

Results

Experiments conducted in two application scenarios show that SudoLM effectively manages user access to parametric knowledge and preserves the overall utility of the LLM for diverse user expertise. The framework demonstrates high precision and recall rates in controlling access to sensitive information, indicating strong performance in protecting privileged data.

Significance

The implementation of SudoLM enhances the functionality of LLMs by allowing differentiated access based on user qualifications, which improves the model's utility for advanced users without compromising access control for others. This is particularly important in risk-sensitive applications, such as healthcare, where responsible information access is crucial to prevent misuse while supporting legitimate queries.

ArXiv Link