Retrieval-Augmented Generation (RAG) techniques have emerged as a promising direction to merge the non-parametric knowledge into Large Language Models (LLMs), thereby alleviating factual errors, hallucinations and outdated knowledge. Existing RAG methods, which append multiple retrieved documents or passages to the input of LLMs, will inevitably increase the context length, resulting in not only significant computational overhead and inference latency, but also performance degradation. Although reranking or compression modules have been introduced to address these challenges, they overlook the contextual preferences of the generative LLMs itself and may inadvertently discard information that is crucial for generation accuracy. To this end, we introduce InnerRAG, which incentivizes RAG via Inner Adaptive Context Selection. InnerRAG is a novel paradigm that empowers LLMs to autonomously select relevant context during generation. Our proposed InnerRAG endows the model to accurately identify the documents that are most helpful for generation from long contexts. By endowing the model with this capability, InnerRAG facilitates more effective exploitation of external knowledge without being misled by disturbed information, leading to substantial improvements in generation quality while maintaining computational efficiency. Extensive experiments across multiple benchmarks and human evaluations demonstrate that our method consistently outperforms state-of-the-art RAG baselines. Moreover, our framework is orthogonal and complementary to in-context RAG approaches, offering further performance improvements when combined.

Backdoor attacks threaten the reliability of NLP models by embedding hidden behaviors during training, which are activated by specific inputs at inference time. Traditional backdoor triggers often rely on explicit content alterations-such as token insertion or stylistic modification-which may compromise semantic coherence and be easily detected.In this work, we propose a novel backdoor attack strategy that leverages the linguistic properties of code-mixing(a language form that combines elements from two or more languages) as implicit triggers. Drawing inspiration from natural code-mixing communication, we design three types of linguistically grounded triggers: inter-word mixing, intra-sentential mixing, and inter-sentential mixing. These forms reflect realistic language usage patterns in bilingual communities, enhancing the stealthiness of the attack. The experiment results show that existing NLP models perform poorly when faced with backdoor attacks based on code-mixing triggers. We are the first to focus on code-mixing as a trigger for text backdoor attacks. We hope this research raises awareness of the vulnerability of models during training when faced with code-mixing.

Retrieval-Augmented Image Captioning enhances the model's understanding of real-world images by retrieving external knowledge. Existing methods mainly use original captions or isolated entities related to the query image to help generate captions. However, these methods make the model either imitate the caption style or fail to capture the relationship between entities, resulting in a lack of diversity or inaccuracy in the generated captions. To address these issues, we propose SEAR, a novel framework that utilizes external Synthesized Entity-Aware knowledge Representations to improve captioning performance. Specifically, SEAR clusters images based on scene-level and entity-level features, and synthesizes each clustered images into representative images as retrieval indexes, and simultaneously utilizes a large model to extract and supplement structured knowledge graphs from the corresponding cluster captions. Furthermore, we design a knowledge-graph pruner to prune the knowledge graph by retaining the most relevant subgraphs to the query image. By undertaking these steps in an integrated manner, SEAR enables the model to acquire non-redundant and structured information for generating captions and avoid data-related privacy issues. Extensive experiments on MSCOCO, Flickr30k, and NoCaps demonstrate the effectiveness of our method both in-domain and out-of-domain, outperforming existing lightweight RAIC methods and remaining competitive with heavyweight models.

Retrieval-augmented generation (RAG) paradigms can integrate external knowledge to enhance and validate the output of Large Language Models (LLMs) thereby mitigating generative hallucinations and broadening the model's knowledge scope. Despite advancements, existing RAG methods still suffer from uncertainty of prediction during the multi-round retrieval-generation process, and a lack of the ability to balance the adequacy and redundancy of retrieved information. To address these challenges, we propose CIRAG, an approach that combines the RAG process with collective intelligence. Inspired by the crowd of wisdom, CIRAG simulates individual independent decision-making and information aggregation within a crowd. Specifically, CIRAG first enhances retrieval diversity by expanding queries based on extracted entities, then combines frequency-based and semantic-based reranking to form a multi granularity fusion reranking thereby assessing better relevance, and integrate multiple information sources for accurate content generation. By undertaking these steps in an integrated manner, CIRAG enables the model to acquire comprehensive and non-redundant information for generating responses. We conduct extensive experiments with HotPotQA and 2WikiMultihopQA datasets, popular benchmark for retrieval-based, multi-step question-answering. Experimental results show that our approach surpasses existing advanced RAG framework while providing high portability in query expansion as well as strong comprehensiveness exhibited in the collective intelligence.

Privacy-preserving content-based image retrieval aims to safeguard the security of outsourced private images while maintaining their searchability. However, existing schemes encounter challenges in striking a balance between security, accuracy, and efficiency, as well as difficulties in scaling to large-scale image retrieval in multi-user settings. In this paper, we propose a novel Efficient Large-Scale Encrypted Image Retrieval (ELSEIR) framework for outsourced data sharing. We first utilize a deep hashing model for image feature extraction. Building upon this, we design an irreversible random hash code generation method that incorporates permutation keys for personalized access and integrates differential privacy to further enhance data security. In our multi-user implementation, we distribute the switch keys to the cloud to standardize each key, enabling the accurate search. In addition, we have theoretically proven that our ELSEIR guarantees both outsourced data security and query user privacy. Extensive experiments on real-world datasets demonstrate that our ELSEIR yields comparable accuracy to the unprotected baseline while outperforming existing methods in terms of both retrieval accuracy and efficiency.

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.

Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.