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LLMIdxAdvis: Resource-Efficient Index Advisor Utilizing Large Language Model

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arXiv 2025年

作者： Zhao, Xinxin Li, Haoyang Zhang, Jing Huang, Xinmei Zhang, Tieying Chen, Jianjun Shi, Rui Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China ByteDance China

Index recommendation is essential for improving query performance in database management systems (DBMSs) through creating an optimal set of indexes under specific constraints. Traditional methods, such as heuristic and learning-based approaches, are effective but face challenges like lengthy recommendation time, resource-intensive training, and poor generalization across different workloads and database schemas. To address these issues, we propose LLMIdxAdvis, a resource-efficient index advisor that uses large language models (LLMs) without extensive fine-tuning. LLMIdxAdvis frames index recommendation as a sequence-to-sequence task, taking target workload, storage constraint, and corresponding database environment as input, and directly outputting recommended indexes. It constructs a high-quality demonstration pool offline, using GPT-4-Turbo to synthesize diverse SQL queries and applying integrated heuristic methods to collect both default and refined labels. During recommendation, these demonstrations are ranked to inject database expertise via in-context learning. Additionally, LLMIdxAdvis extracts workload features involving specific column statistical information to strengthen LLM’s understanding, and introduces a novel inference scaling strategy combining vertical scaling (via "Index-Guided Major Voting" and Best-of-N) and horizontal scaling (through iterative "self-optimization" with database feedback) to enhance reliability. Experiments on 3 OLAP and 2 real-world benchmarks reveal that LLMIdxAdvis delivers competitive index recommendation with reduced runtime, and generalizes effectively across different workloads and database schemas. Copyright © 2025, The Authors. All rights reserved.

关键词： Query languages

PCQPR: Proactive Conversational Question Planning with Reflection

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arXiv 2024年

作者： Guo, Shasha Liao, Lizi Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore

Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self- Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies. This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome. Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems. © 2024, CC BY-NC-SA.

关键词： Direct process refining

QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

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QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

How to Mitigate Information Loss in knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback

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arXiv 2025年

作者： Huang, Manzong Bu, Chenyang He, Yi Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology Ministry of Education China School of Data Science William & Mary WilliamsburgVA United States

knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where it achieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors. © 2025, CC BY.

关键词： knowledge graph

Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch

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arXiv 2023年

作者： Du, Pan Zhao, Suyun Sheng, Zisen Li, Cuiping Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China China Renmin University of China Beijing China

Semi-Supervised Learning (SSL) under class distribution mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown categories unseen in the labeled ones. In such mismatch scenarios, traditional SSL suffers severe performance damage due to the harmful invasion of the instances with unknown categories into the target classifier. In this study, by strict mathematical reasoning, we reveal that the SSL error under class distribution mismatch is composed of pseudo-labeling error and invasion error, both of which jointly bound the SSL population risk. To alleviate the SSL error, we propose a robust SSL framework called Weight-Aware Distillation (WAD) that, by weights, selectively transfers knowledge beneficial to the target task from unsupervised contrastive representation to the target classifier. Specifically, WAD captures adaptive weights and high-quality pseudo-labels to target instances by exploring point mutual information (PMI) in representation space to maximize the role of unlabeled data and filter unknown categories. Theoretically, we prove that WAD has a tight upper bound of population risk under class distribution mismatch. Experimentally, extensive results demonstrate that WAD outperforms five state-of-the-art SSL approaches and one standard baseline on two benchmark datasets, CIFAR10 and CIFAR100, and an artificial cross-dataset. The code is available at https://***/RUC-DWBI-ML/research/tree/main/WAD-master. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Yuxin Zhang, Lizhong Zhu, Tao Luo, Wenjian School of Computer Science and Information Engineering Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230027 China School of Computer Science and Information Engineering University of South China Hunan Hengyang421200 China School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9798350337020

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to 'track the moving optima', rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://***/DMiC-Lab-HFUT/SMDE-Transfer. © 2023 IEEE.

关键词：

Classifier Belief Optimization for Visual Categorization 27th

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Classifier Belief Optimization for Visual Categorization

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Yang, Gang Li, Xirong Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783030678319

Classifier belief represents the confidence of a classifier making judgment about a special instance. Based on classifier belief, we propose an approach to realize classifier belief optimization. Through enriching prior knowledge and thus reducing the scope of candidate classes, our approach improves classification accuracy. A feature perturbation strategy containing an objective optimization is developed to automatically generate labeled instances. Moreover, we propose a classifier consensus strategy (CCS) for classifier optimization. CCS enables a given classifier to take full advantage of the test data to enrich prior knowledge. Experiments on three benchmark datasets and three classical classifiers justify the validity of the proposed approach. We improve the classification accuracy of a linear SVM by 6%. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

Fine-Grained Generation for Zero-Shot Learning 27th

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Fine-Grained Generation for Zero-Shot Learning

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Sun, Weimin Xu, Jieping Yang, Gang Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783030678319

Although zero-shot learning (ZSL) has achieved success in recognizing unseen classes images, most previous studies focus on feature projection from one domain to another, neglecting the domain shift problem caused by differences in classes. In this paper, we propose two novel methods in terms of attributes and features for ZSL problem. The first is Sample-Level Attribute Generation via GAN (SLA-GAN) that generates sample-level attributes. Considering the influence of lighting, environment, weather and other factors, the description based on class-level attributes will be biased. SLA-GAN generates sample-level attributes to describe each sample. Specially, we train a conditional Wasserstein GAN in which the generator synthesizes sample-level attributes from features and the discriminator distinguishes the sample-level from class-level attributes. Apart from that, we propose a novel method Fine-Grained Sample Generation via GAN (FGS-GAN) which can generate the unseen samples from the most similar samples of seen classes. Finally, we replace the class-level attributes with sample-level attributes in FGS-GAN and analyze the effects of sample-level attributes on synthesized features. Experiments on five benchmark data set show that our models outperform state-of-the-art methods in ZSL. © 2021, Springer Nature Switzerland AG.

关键词： Zero-shot learning