Test-time computing approaches, which leverage additional computational resources during inference, have been proven effective in enhancing large language model performance. This work introduces a novel, linearly scaling approach, TestNUC, that improves test-time predictions by leveraging the local consistency of neighboring unlabeled data-it classifies an input instance by considering not only the model’s prediction on that instance but also on neighboring unlabeled instances. We evaluate TestNUC across eight diverse datasets, spanning intent classification, topic mining, domain discovery, and emotion detection, demonstrating its consistent superiority over baseline methods such as standard prompting and self-consistency. Furthermore, TestNUC can be seamlessly integrated with existing test-time computing approaches, substantially boosting their performance. Our analysis reveals that TestNUC scales effectively with increasing amounts of unlabeled data and performs robustly across different embedding models, making it practical for real-world applications. Our code is available at https://github.com/HenryPengZou/TestNUC.

With the rapid development of Natural Language Understanding for information retrieval, fine-tuned deep language models, e.g., BERT-based, perform remarkably effective in passage searching tasks. To lower the architecture complexity, the recent state-of-the-art model ColBERT employs Contextualized Late Interaction paradigm to independently learn fine-grained query-passage representations. Apart from the architecture simplification, embedding binarization, as another promising branch in model compression, further specializes in the reduction of memory and computation overheads. In this concise paper, we propose an effective post-training embedding binarization approach over ColBERT, achieving both architecture-level and embedding-level optimization for online inference. The empirical results demonstrate the efficaciousness of our proposed approach, empowering it to perform online query-passage matching acceleration.