MOGIC: Metadata-Infused Oracle Guidance for Improved Extreme Classification - Prabhu, Suchith Chidananda; Singh, Bhavyajeet; Mittal, Anshul; Asokan, Siddarth; Mohan, Shikhar; Saini, Deepak; Prabhu, Yashoteja; Kumar, Lakshya; Jiao, Jian; Singh, Amit; Tandon, Niket; Gupta, Manish; et al. (ICML 2025, 2025)
Retrieval-augmented classification and generation models benefit from early-stage fusion of high-quality text-based metadata, often called memory, but face high latency and noise sensitivity. In extreme classification (XC), where low latency is crucial, existing methods use late-stage fusion for efficiency and robustness. To enhance accuracy while maintaining low latency, we propose MOGIC, a novel approach to metadata-infused oracle guidance for XC. We train an early-fusion oracle classifier with access to both query-side and label-side ground-truth metadata in textual form and subsequently use it to guide existing memory-based XC disciple models via regularization. The MOGIC algorithm improves precision@1 and propensity-scored precision@1 of XC disciple models by 1-2% on six standard datasets, at no additional inference-time cost. We show that MOGIC can be used in a plug-and-play manner to enhance memory-free XC models such as NGAME or DEXA. Lastly, we demonstrate the robustness of the MOGIC algorithm to missing and noisy metadata. The code is publicly available at https://github.com/suchith720/mogic.