Text-to-visualization (text-to-vis) models for tabular data have become essential tools in the era of big data, enabling users to generate visualizations and make data-driven decisions through natural language queries (NLQs). Despite their growing adoption, the security vulnerabilities of these models remain largely unexplored. To address this gap, we propose VisPoison, a backdoor attack framework that realistically simulates three types of attacks on text-to-vis models via data poisoning: data exposure, misleading visualizations, and denial-of-service (DoS). Specifically, VisPoison introduces two types of stealthy triggers to enable both proactive and passive backdoor activations. Proactive triggers are deliberately inserted by attackers using rare-word patterns to extract sensitive information, whereas passive triggers are unintentionally activated by users through first-word prompts, resulting in visualization errors or DoS failures. To support these triggers, we craft specialized payloads for visualization queries that allow compromised models to function normally on benign inputs while producing malicious outputs in the presence of triggers. Extensive evaluations on both trainable and in-context learning (ICL)-based text-to-vis models show that VisPoison achieves attack success rates exceeding 90\%, exposing serious vulnerabilities. Additionally, existing defense strategies reveal limited effectiveness against VisPoison, underscoring the urgent need for more robust and security-aware text-to-vis systems to safeguard human-data interaction.

As search engines increasingly adopt answer-centric interfaces, AI-generated summaries are often consumed as final answers. These summaries are typically well-structured and citation-rich, creating a strong appearance of reliability that can encourage user trust, even when evidential grounding is uncertain. Motivated by this gap between appearance and evidence, we analyze 14,175 real-world queries from MS MARCO by examining Google Search AI summaries and their cited sources. We find that reliable-looking summaries mask substantial evidential failures. Despite their credible appearance, 32.31% of summaries are incorrect, and 56.16% of these errors arise even when supporting evidence exists in the cited sources. Moreover, 31.08% of summaries exhibit citation inconsistencies, with conflict risk increasing as more sources are cited and as citations appear in more prominent positions. Our findings reveal systematic, user-facing risks in answer-centric search, highlighting the need for evaluation and design practices beyond surface-level reliability signals. https://github.com/icip-cas/AISummary.

The proliferation of complex non-factoid questions in modern information seeking (IS) systems exposes critical limitations in conventional Retrieval-Augmented Generation (RAG) approaches, particularly their static search strategies and the lack of systematic multi-source information integration capabilities. Facing these limitations, we present PASS (Proactive Agent-driven Search System), a novel multi-agent framework that operationalizes human-like proactive search strategies through five specialized agents: Revealer for intent analysis, Navigator for search planning, Seeker/Reader for adaptive retrieval, and Writer for response synthesis, systematically expanding the search space through iterative query refinement and multi-perspective knowledge integration. Crucially, our framework demonstrates remarkable adaptability to mid-sized LLMs, demonstrating its scalability in resource-constrained environments. To comprehensively assess the effectiveness of the proposed framework, we carry out extensive experiments on both mid-sized and proprietary large-scale LLMs, evaluating response quality for complex non-factoid questions using a newly introduced nugget-based assessment. Experimental results from offline nugget-based evaluation and online A/B Tests confirm substantial improvements in answer quality, advancing proactive information seeking methodologies and offering practical pathways for democratizing complex reasoning capabilities to resource-constrained environments.

Abstract We are currently in the epoch of Large Language Models (LLMs), which have transformed numerous technological domains within the database community. In this paper, we examine the application of LLMs in text-to-visualization (text-to-vis). The advancement of natural language processing technologies has made natural language interfaces more accessible and intuitive for visualizing tabular data. However, despite utilizing advanced neural network architectures, current methods such as Seq2Vis, ncNet, and RGVisNet for transforming natural language queries into DV commands still underperform, indicating significant room for improvement. In this paper, we introduce Prompt4Vis , a novel framework that leverages LLMs and In-context learning to enhance the generation of data visualizations from natural language. Given that In-context learning’s effectiveness is highly dependent on the selection of examples, it is critical to optimize this aspect. Additionally, encoding the full database schema of a query is not only costly but can also lead to inaccuracies. This framework includes two main components: (1) an example mining module that identifies highly effective examples to enhance In-context learning capabilities for text-to-vis applications, and (2) a schema filtering module designed to streamline database schemas. Comprehensive testing on the NVBench dataset has shown that Prompt4Vis significantly outperforms the current state-of-the-art model, RGVisNet, by approximately 35.9% on development sets and 71.3% on test sets. To the best of our knowledge, Prompt4Vis is the first framework to incorporate In-context learning for enhancing text-to-vis, marking a pioneering step in the domain.

Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph. Current KGC research mostly follows an entity ranking protocol, wherein the effectiveness is measured by the predicted rank of a masked entity in a test triple. The overall performance is then given by a micro(-average) metric over all individual answer entities. Due to the incomplete nature of the large-scale knowledge bases, such an entity ranking setting is likely affected by unlabelled top-ranked positive examples, raising questions on whether the current evaluation protocol is sufficient to guarantee a fair comparison of KGC systems. To this end, this paper presents a systematic study on whether and how the label sparsity affects the current KGC evaluation with the popular micro metrics. Specifically, inspired by the TREC paradigm for large-scale information retrieval (IR) experimentation, we create a relatively "complete" judgment set based on a sample from the popular FB15k-237 dataset following the TREC pooling method. According to our analysis, it comes as a surprise that switching from the original labels to our "complete" labels results in a drastic change of system ranking of a variety of 13 popular KGC models in terms of micro metrics. Further investigation indicates that the IR-like macro(-average) metrics are more stable and discriminative under different settings, meanwhile, less affected by label sparsity. Thus, for KGC evaluation, we recommend conducting TREC-style pooling to balance between human efforts and label completeness, and reporting also the IR-like macro metrics to reflect the ranking nature of the KGC task.

Online search latency is a major bottleneck in deploying large-scale pre-trained language models, e.g. BERT, in retrieval applications. Inspired by the recent advances in transformer-based document expansion technique, we propose to trade offline relevance weighting for online retrieval efficiency by utilizing the powerful BERT ranker to weight the neighbour documents collected by generated pseudo-queries for each document. In the online retrieval stage, the traditional query-document matching is reduced to the much less expensive query to pseudo-query matching, and a document rank list is quickly recalled according to the pre-computed neighbour documents. Extensive experiments on the standard MS MARCO dataset with both passage and document ranking tasks demonstrate promising results of our method in terms of both online efficiency and effectiveness.