The human-level fluency achieved by large language models in text generation has intensified the challenge of distinguishing between human-written and AI-generated texts. While current fine-tuned detectors exist, they often lack robustness against adversarial attacks and struggle with out-of-distribution topics, limiting their practical applicability. This study introduces AIDER, a robust and topic-independent AI-generated text detection framework. AIDER leverages the ALBERT model for topic content disentanglement, enhancing transferability to unseen topics. It incorporates an augmentor that generates robust adversarial data for training, coupled with contrastive learning techniques to boost resilience. Comprehensive experiments demonstrate AIDER’s significant superiority over state-of-the-art methods, exhibiting exceptional robustness against adversarial attacks with minimal performance degradation. AIDER consistently achieves high accuracy in non-augmented scenarios and demonstrates remarkable generalizability to unseen topics. These attributes establish AIDER as a powerful and versatile tool for LLM-generated text detection across diverse real-world applications, addressing critical challenges in the evolving landscape of AI-generated content.

In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of textual descriptions and protein sequences. Through a thorough analysis of existing model architectures and text-based protein understanding benchmarks, we identify significant data leakage issues present in current benchmarks. Moreover, conventional metrics derived from natural language processing fail to assess the model’s performance in this domain accurately. To address these limitations, we reorganize existing datasets and introduce a novel evaluation framework based on biological entities. Motivated by our observation, we propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. Our code and data will be available.