Yanzhou Pan, Huawei Lin, Yide Ran, Jiamin Chen, Xiaodong Yu, Weijie Zhao, Denghui Zhang, Zhaozhuo Xu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The burgeoning size of modern text datasets has heightened the need for efficient text retrieval systems. For such applications, Approximate Nearest Neighbor (ANN) search algorithms, and in particular graph-based methods have long been established as the leading approach in terms of recall and search speed. However, the data and execution dependencies of vertices increase the construction workload and complicate maintenance processes for the constructed index. In this paper, we present Locality-Sensitive Indexing for Graph-Based Search (or LIGS), which utilizes independent locality-sensitive hashing algorithms to simulate a proximity graph, on which a standard graph search can be performed. We show that LIGS offers substantially faster maintenance (insertion/deletion) speeds and better conservation of graph quality compared to state-of-the-art graph-based ANN methods, demonstrating LIGS as a promising alternative for maintenance-heavy scenarios.

Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation?In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data.The proposed framework consists of two stages: caching and retrieval.First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory.Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup.Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval.Our empirical result confirms the efficiency and effectiveness of RapidIn.

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss.

Various machine learning applications take users' data to train the models. Recently enforced legislation requires companies to remove users' data upon requests, i.e.,the right to be forgotten. In the context of machine learning, the trained model potentially memorizes the training data. Machine learning algorithms have to be able to unlearn the user data that are requested to delete to meet the requirement. Gradient Boosting Decision Trees (GBDT) is a widely deployed model in many machine learning applications. However, few studies investigate the unlearning on GBDT. This paper proposes a novel unlearning framework for GBDT. To the best of our knowledge, this is the first work that considers machine unlearning on GBDT. It is not straightforward to transfer the unlearning methods of DNN to GBDT settings. We formalized the machine unlearning problem and its relaxed version. We propose an unlearning framework that efficiently and effectively unlearns a given collection of data without retraining the model from scratch. We introduce a collection of techniques, including random split point selection and random partitioning layers training, to the training process of the original tree models to ensure that the trained model requires few subtree retrainings during the unlearning. We investigate the intermediate data and statistics to store as an auxiliary data structure during the training so that we can immediately determine if a subtree is required to be retrained without touching the original training dataset. Furthermore, a lazy update technique is proposed as a trade-off between unlearning time and model functionality. We experimentally evaluate our proposed methods on public datasets. The empirical results confirm the effectiveness of our framework.