Robust Retrieval

Robust retrieval focuses on improving the reliability and accuracy of information retrieval systems, particularly in the face of noisy data, adversarial attacks, and out-of-distribution examples. Current research emphasizes enhancing neural retrieval models, including dense retrieval models and those integrated with large language models (LLMs), through techniques like adversarial training, consistency learning, and progressive learning to improve robustness. These advancements are crucial for building trustworthy search engines and improving the performance of retrieval-augmented applications across diverse domains, from question answering to image captioning and fact verification. The development of robust retrieval methods is vital for ensuring the reliability and effectiveness of numerous AI applications that rely on accurate and efficient information access.