Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks

Text data are often encoded as dense vectors, known as embeddings, which capture semantic, syntactic, contextual, and domain-specific information. These embeddings, widely adopted in various applications, inherently contain rich information that may be susceptible to leakage under certain attacks. The GEIA framework highlights vulnerabilities in sentence embeddings, demonstrating that they can reveal the original sentences they represent. In this study, we reproduce GEIA's findings across various neural sentence embedding models. Additionally, we contribute new analysis to examine whether these models leak sensitive information from their training datasets. We propose a simple yet effective method without any modification to the attacker's architecture proposed in GEIA. The key idea is to examine differences between log-likelihood for masked and original variants of data that sentence embedding models have been pre-trained on, calculated on the embedding space of the attacker. Our findings indicate that following our approach, an adversary party can recover meaningful sensitive information related to the pre-training knowledge of the popular models used for creating sentence embeddings, seriously undermining their security. Our code is available on: https://github.com/taslanidis/GEIA

On the Reproducibility of Learned Sparse Retrieval Adaptations for Long Documents

Document retrieval is one of the most challenging tasks in Information Retrieval. It requires handling longer contexts, often resulting in higher query latency and increased computational overhead. Recently, Learned Sparse Retrieval (LSR) has emerged as a promising approach to address these challenges. Some have proposed adapting the LSR approach to longer documents by aggregating segmented document using different post-hoc methods, including n-grams and proximity scores, adjusting representations, and learning to ensemble all signals. In this study, we aim to reproduce and examine the mechanisms of adapting LSR for long documents. Our reproducibility experiments confirmed the importance of specific segments, with the first segment consistently dominating document retrieval performance. Furthermore, We re-evaluate recently proposed methods -- ExactSDM and SoftSDM -- across varying document lengths, from short (up to 2 segments) to longer (3+ segments). We also designed multiple analyses to probe the reproduced methods and shed light on the impact of global information on adapting LSR to longer contexts. The complete code and implementation for this project is available at: https://github.com/lionisakis/Reproducibilitiy-lsr-long.