Improving Multitask Retrieval by Promoting Task Specialization
In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outperforms task-specific retrievers by promoting task specialization. The main ingredients are: (1) a better choice of pretrained model (one that is explicitly optimized for multitasking) along with compatible prompting, and (2) a novel adaptive learning method that encourages each parameter to specialize in a particular task. The resulting multitask retriever is highly performant on the KILT benchmark. Upon analysis, we find that the model indeed learns parameters that are more task-specialized compared to naive multitasking without prompting or adaptive learning.
Code (1)
Tasks
RetrievalSimilar Papers 제목 키워드 기반
ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization
Mixture-of-Experts (MoE) architectures expand model capacity by sparsely activating experts but face two core challenges: misalignment between router logits and each expert's internal structure leads to unstable routing …
Text RetrievalContest in Multitasking: An Evidence from Chinese County Officials' Promotion Assessment
Real-world observed contests often take the form of multi-task contests rather than single-task contests, and existing theories are insufficient to explain the incentive for extending the task dimension. This paper propo…
Brain Tumor Image Retrieval via Multitask Learning
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting feature space as a similarity metric. H…
ClassificationGeneral ClassificationImage RetrievalRetrievalMemeLens: Multilingual Multitask VLMs for Memes
Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme research is distributed across tasks (ha…
Domain GeneralizationRAVEN: Multitask Retrieval Augmented Vision-Language Learning
The scaling of large language models to encode all the world's knowledge in model parameters is unsustainable and has exacerbated resource barriers. Retrieval-Augmented Generation (RAG) presents a potential solution, yet…
Image CaptioningRAGRetrievalRetrieval-augmented Generation+1