Multi-step retrieval-augmented generation has attracted increasing attention due to its capacity to improve the factuality of large language models with iterative retrieved knowledge. However, the performance of multi-step RAG systems is susceptible to potential retrieval noise and fabricated documents in real-world scenarios. Current approaches usually utilize supervised fine-tuning on predetermined noisy contexts to enhance the robustness. However, their performance remains inadequate when it comes to more complicated long-context scenarios due to the lack of adaptability. Towards this end, we propose a novel framework named Context-attended Adversarial Reinforcement Learning (CARE) for multi-step RAG systems against attacks. The core of our CARE is to conduct reinforcement learning on adversarial samples which are alternatingly enhanced with text gradients. In particular, our CARE includes a reward model to identify the accuracy of responses, which is minimized for the generation of adversarial samples with text gradients. These context-attended noisy samples are then utilized for reinforcement learning to maximize the rewards. The whole framework is conducted alternatingly from easy to hard samples to ensure the smoothness of the optimization. Extensive experiments on multi-step RAG benchmark datasets are conducted to validate the superiority of our proposed CARE in multiple noisy scenarios. Our code is available at https://github.com/yingtaoren/CARE.

A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.