While Large Language Models (LLMs) demonstrate impressive proficiency in generating SQL queries, they fundamentally lack the capability to self-evaluate correctness without an execution oracle.This limitation creates a stark Generation-Selection Gap, where high potential accuracy (Pass@K) fails to translate into execution accuracy (Pass@1).Although supervised verifiers offer mitigation, they incur prohibitive annotation costs and suffer from domain fragility.Consequently, recent research has pivoted to the training-free setting.However, existing methods-such as Self-Consistency or LLM-as-a-Judge-remain hampered by systematic bias (consensus on hallucinations) and symbolic blindness (inability to simulate execution states).We introduce DPC (Dual-Paradigm Consistency), a multiagent framework that reformulates SQL selection from a probabilistic guessing task on hidden data into a deterministic verification task on visible data.Specifically, DPC employs a SLICER and a TESTER agent to collaboratively construct a Minimal Distinguishing Database (MDD)-an adversarial, fully observable micro-environment engineered to expose logical discrepancies between candidates.To break the self-correction bias, a SOLVER agent then verifies the SQL candidates by crossreferencing their execution against a parallel Python/Pandas solution.By validating execution consistency between declarative (SQL) and imperative (Python) paradigms, DPC robustly discriminates correct logic from systematic hallucinations.Experiments on BIRD and Spider across multiple LLMs demonstrate that our method consistently outperforms existing selection baselines, achieving absolute accuracy improvements of up to 2.2% over strong competitors like Self-Consistency.

Execution Accuracy (EX), the widely used metric for evaluating the effectiveness of Natural Language to SQL (NL2SQL) solutions, is becoming increasingly unreliable.It is sensitive to syntactic variation, ignores that questions may admit multiple interpretations, and is easily misled by erroneous ground-truth SQL.To address this, we introduce ROSE, an intentcentered metric that focuses on whether the predicted SQL answers the question, rather than consistency with the ground-truth SQL under the reference-dependent paradigm.ROSE employs an adversarial Prover-Refuter cascade: SQL Prover assesses the semantic correctness of a predicted SQL against the user's intent independently, while Adversarial Refuter uses the ground-truth SQL as evidence to challenge and refine this judgment.On our expert-aligned validation set ROSE-VEC, ROSE achieves the best agreement with human experts, outperforming the next-best metric by nearly 24% in Cohen's Kappa.We also conduct a largescale re-evaluation of 19 NL2SQL methods, revealing four valuable insights.We release ROSE and ROSE-VEC to facilitate more reliable NL2SQL research 1 .

Although data visualization is powerful for revealing patterns and communicating insights, creating effective visualizations requires familiarity with authoring tools and often disrupts the analysis flow. While large language models show promise for automatically converting analysis intent into visualizations, existing methods function as black boxes without transparent reasoning processes, which prevents users from understanding design rationales and refining suboptimal outputs. To bridge this gap, we propose integrating Chain-of-Thought (CoT) reasoning into the Natural Language to Visualization (NL2VIS) pipeline. First, we design a comprehensive CoT reasoning process for NL2VIS and develop an automatic pipeline to equip existing datasets with structured reasoning steps. Second, we introduce nvBench-CoT, a specialized dataset capturing detailed step-by-step reasoning from ambiguous natural language descriptions to finalized visualizations, which enables state-of-the-art performance when used for model fine-tuning. Third, we develop DeepVIS, an interactive visual interface that tightly integrates with the CoT reasoning process, allowing users to inspect reasoning steps, identify errors, and make targeted adjustments to improve visualization outcomes. Quantitative benchmark evaluations, two use cases, and a user study collectively demonstrate that our CoT framework effectively enhances NL2VIS quality while providing insightful reasoning steps to users.

In this paper, we introduce the concept of realistic charts, referring to charts in the real world that cannot be digitally altered, such as those printed in newspapers or used in PDF documents. By enabling interaction with and virtual enhancement of these realistic charts as if they were digital, we transform realistic charts into "digital charts"by adding virtual overlays. To achieve this, we identify 34 overlay strategies (e.g., highlights and trendlines) for five widely-used chart types (e.g., line charts) through systematic exploration and a formative study. To simplify overlay creation, we introduce a new grammar named Vega-Overlay. Leveraging this design space and grammar, we develop a system called HARVis, which allows users to generate virtual overlays through augmented reality devices using speech and optional gestures. A user study involving 33 participants from diverse fields, across 17 tasks, demonstrates the effectiveness and usability of HARVis.

Text-to-SQL, which enables natural language interaction with databases, serves as a pivotal method across diverse industries. With new, more powerful large language models (LLMs) emerging every few months, fine-tuning has become incredibly costly, labor-intensive, and error-prone. As an alternative, *zero-shot* Text-to-SQL, which leverages the growing knowledge and reasoning capabilities encoded in LLMs without task-specific fine-tuning, presents a promising and more challenging direction. To address this challenge, we propose Alpha-SQL, a novel approach that leverages a Monte Carlo Tree Search (MCTS) framework to iteratively infer SQL construction actions based on partial reasoning states. To enhance the framework’s reasoning capabilities, we introduce *LLM-as-Action-Model* to dynamically generate SQL construction *actions* during the MCTS process, steering the search toward more promising SQL queries. Moreover, Alpha-SQL employs a self-supervised reward function to evaluate the quality of candidate SQL queries, ensuring more accurate and efficient query generation. Experimental results show that Alpha-SQL achieves 69.7% execution accuracy on the BIRD development set, using a 32B open-source LLM without fine-tuning. Alpha-SQL outperforms the best previous zero-shot approach based on GPT-4o by 2.5% on the BIRD development set.

Natural Language to SQL (i.e., NL2SQL) translation is crucial for democratizing database access, but even state-of-the-art models frequently generate semantically incorrect SQL queries, hindering the widespread adoption of these techniques by database vendors. While existing NL2SQL benchmarks primarily focus on correct query translation, we argue that a benchmark dedicated to identifying common errors in NL2SQL translations is equally important, as accurately detecting these errors is a prerequisite for any subsequent correction-whether performed by humans or models. To address this gap, we propose NL2SQL-BUGs, the first benchmark dedicated to detecting and categorizing semantic errors in NL2SQL translation. NL2SQL-BUGs adopts a two-level taxonomy to systematically classify semantic errors, covering 9 main categories and 31 subcategories. The benchmark consists of 2,018 expert-annotated instances, each containing a natural language query, database schema, and SQL query, with detailed error annotations for semantically incorrect queries. Through comprehensive experiments, we demonstrate that current large language models exhibit significant limitations in semantic error detection, achieving an average detection accuracy of 75.16%. Specifically, our method successfully detected 106 errors (accounting for 6.91%) in BIRD, a widely-used NL2SQL dataset, which were previously undetected annotation errors. This highlights the importance of semantic error detection in NL2SQL systems. The benchmark is publicly available at https://nl2sql-bugs.github.io/.

Text-to-Visualization (Text2VIS) enables users to create visualizations from natural language queries, making data insights more accessible. However, Text2VIS faces challenges in interpreting ambiguous queries, as users often express their visualization needs in imprecise language. To address this challenge, we introduce nBench 2.0, a new benchmark designed to evaluate Text2VIS systems in scenarios involving ambiguous queries. nvBench 2.0 includes 7,878 natural language queries and 24,076 corresponding visualizations, derived from 780 tables across 153 domains. It is built using a controlled ambiguity-injection pipeline that generates ambiguous queries through a reverse-generation workflow. By starting with unambiguous seed visualizations and selectively injecting ambiguities, the pipeline yields multiple valid interpretations for each query, with each ambiguous query traceable to its corresponding visualization through step-wise reasoning paths.We evaluate various Large Language Models (LLMs) on their ability to perform ambiguous Text2VIS tasks using nBench 2.0. We also propose Step-Text2Vis, an LLM-based model trained on nvBench 2.0, which enhances performance in ambiguous scenarios through step-wise preference optimization. Our results show that Step-Text2Vis outperforms all baselines, setting a new state-of-the-art for ambiguous Text2VIS tasks. Our source code and data are available at https://nvbench2.github.io/

Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of Text-to-SQL has been greatly enhanced with the emergence of Large Language Models (LLMs). In this survey, we provide a comprehensive review of Text-to-SQL techniques powered by LLMs, covering its entire lifecycle from the following four aspects: (1)Model:Text-to-SQL translation techniques that tackle not only NL ambiguity and under-specification, but also properly map NL with database schema and instances; (2)Data:From the collection of training data, data synthesis due to training data scarcity, to Text-to-SQL benchmarks; (3)Evaluation:Evaluating Text-to-SQL methods from multiple angles using different metrics and granularities; and (4)Error Analysis:analyzing Text-to-SQL errors to find the root cause and guiding Text-to-SQL models to evolve. Moreover, we offer a rule of thumb for developing Text-to-SQL solutions. Finally, we discuss the research challenges and open problems of Text-to-SQL in the LLMs era.

Advancements in adapting deep convolution architectures for spiking neural networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of multiplication-free inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in multilayer perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization (BN) to retain MFI compatibility and introduce a spiking patch encoding (SPE) layer to enhance local feature extraction capabilities. As a result, we establish an efficient multistage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pretraining or sophisticated SNN training techniques, our network secures a top-one accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-one accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.

Large Language Models (LLMs) have demonstrated impressive capabilities across a range of scientific tasks including mathematics, physics, and chemistry. Despite their successes, the effectiveness of LLMs in handling complex statistical tasks remains systematically under-explored. To bridge this gap, we introduce StatQA, a new benchmark designed for statistical analysis tasks. StatQA comprises 11,623 examples tailored to evaluate LLMs' proficiency in specialized statistical tasks and their applicability assessment capabilities, particularly for hypothesis testing methods. We systematically experiment with representative LLMs using various prompting strategies and show that even state-of-the-art models such as GPT-4o achieve a best performance of only 64.83%, indicating significant room for improvement. Notably, while open-source LLMs (e.g. LLaMA-3) show limited capability, those fine-tuned ones exhibit marked improvements, outperforming all in-context learning-based methods (e.g. GPT-4o). Moreover, our comparative human experiments highlight a striking contrast in error types between LLMs and humans: LLMs primarily make applicability errors, whereas humans mostly make statistical task confusion errors. This divergence highlights distinct areas of proficiency and deficiency, suggesting that combining LLM and human expertise could lead to complementary strengths, inviting further investigation into their collaborative potential. Our source code and data are available at https://statqa.github.io/.

Translating users' natural language questions into SQL queries ( i.e. , nl2sql) significantly lowers the barriers to accessing relational databases. The emergence of Large Language Models has introduced a novel paradigm in nl2sql tasks, enhancing capabilities dramatically. However, this raises a critical question: Are we fully prepared to deploy nl2sql models in production? To address the posed questions, we present a multi-angle nl2sql evaluation framework, NL2SQL360 , to facilitate the design and test of new nl2sql methods for researchers. Through NL2SQL360 , we conduct a detailed comparison of leading nl2sql methods across a range of application scenarios, such as different data domains and sql characteristics, offering valuable insights for selecting the most appropriate nl2sql methods for specific needs. Moreover, we explore the nl2sql design space, leveraging NL2SQL360 to automate the identification of an optimal nl2sql solution tailored to user-specific needs. Specifically, NL2SQL360 identifies an effective nl2sql method, SuperSQL , distinguished under the Spider dataset using the execution accuracy metric. Remarkably, SuperSQL achieves competitive performance with execution accuracy of 87 % and 62.66 % on the Spider and BIRD test sets, respectively.