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Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Tetiana Gladkykh, Kyrylo Kirykov
https://arxiv.org/abs/2506.12234 https://

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich K…

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2506.11603 https://

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical …

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
https://arxiv.org/abs/2506.11266

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Large language models (LLMs) are routinely deployed as agentic systems, with access to tools that interact with live environments to accomplish tasks. In enterprise deployments these systems need to interact with API collections that can be extremely large and complex, often backed by databases. In order to create datasets with such characteristics, we explore how existing NL2SQL (Natural Language to SQL query) datasets can be used to automatically create NL2API datasets. Specifically, this wor…

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Ziyi Zhang, Ziheng Jiang, Chengquan Jiang, Menghan Yu, Size Zheng, Haibin Lin, Henry Hoffmann, Xin Liu
https://arxiv.org/abs/2506.11309

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Low-latency decoding for large language models (LLMs) is crucial for applications like chatbots and code assistants, yet generating long outputs remains slow in single-query settings. Prior work on speculative decoding (which combines a small draft model with a larger target model) and tensor parallelism has each accelerated decoding. However, conventional approaches fail to apply both simultaneously due to imbalanced compute requirements (between draft and target models), KV-cache inconsistenc…

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen, Xi Ye
https://arxiv.org/abs/2506.09944

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Recent work has identified retrieval heads (Wu et al., 2025b), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHEAD (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHEAD by aggregating attention scores with respect to the input query, using a handful of exa…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Ailin Huang, Bingxin Li, Bruce Wang, Boyong Wu, Chao Yan, Chengli Feng, Heng Wang, Hongyu Zhou, Hongyuan Wang, Jingbei Li, Jianjian Sun, Joanna Wang, Mingrui Chen, Peng Liu, Ruihang Miao, Shilei Jiang, Tian Fei, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Ge, Zheng Gong, Zhewei Huang, Zixin Zhang, Bin Wang, Bo Li, Buyun Ma, Changxin Miao, Changyi Wan, Chen Xu, Dapeng Shi, Dingyuan Hu, Enle…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate natural speech responses directly, hindering seamless audio interactions. To address this, we introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction, a 130-billion-parameter…

This https://arxiv.org/abs/2505.14690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

SGL: A Structured Graphics Language
This paper introduces SGL, a graphics language that is aesthetically similar to SQL. SGL is based on traditional grammars of graphics, as well as Vega-Lite's composition algebra. SGL demonstrates that the grammatical approach to graphics lends itself naturally to a SQL-like language. As a graphical counterpart to SQL, SGL facilitates the addition of visualization capabilities to SQL query interfaces. This paper presents components of the SGL language alongside examples. Comparisons to SQL and e…

Training-Free Query Optimization via LLM-Based Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Boorlakov
https://arxiv.org/abs/2506.05853 https…

Training-Free Query Optimization via LLM-Based Plan Similarity
Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQL query execution without the need for additional model training. We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting. A lightweight cons…

I feel like ChatGPT is taking a jab at other types of models here.

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
Lingyuan Liu, Mengxiang Zhang
https://arxiv.org/abs/2506.04760

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
Large Language Models (LLMs) have shown potential in generating hypothetical documents for query expansion, thereby enhancing information retrieval performance. However, the efficacy of this method is highly dependent on the quality of the generated documents, which often requires complex prompt strategies and the integration of advanced dense retrieval techniques. This can be both costly and computationally intensive. To mitigate these limitations, we explore the use of zero-shot LLM-based que…

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models
Philip Quirke, Narmeen Oozeer, Chaithanya Bandi, Amir Abdullah, Jason Hoelscher-Obermaier, Jeff M. Phillips, Joshua Greaves, Clement Neo, Fazl Barez, Shriyash Upadhyay
https://arxiv.org/abs/2506.00051

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models
This position paper argues that the prevailing trajectory toward ever larger, more expensive generalist foundation models controlled by a handful of big companies limits innovation and constrains progress. We challenge this approach by advocating for an "Expert Orchestration" framework as a superior alternative that democratizes LLM advancement. Our proposed framework intelligently selects from thousands of existing models based on query requirements and decomposition, focusing on identifying w…

This https://arxiv.org/abs/2411.06426 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains
As the integration of the Large Language Models (LLMs) into various applications increases, so does their susceptibility to misuse, raising significant security concerns. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks mainly rely on scenario camouflage, prompt obfuscation, prompt optimization, and prompt iterative optimization to conceal malicious prompts. In particular, sequential prompt chains in a single query can lead LLMs to …

This https://arxiv.org/abs/2505.24226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

E^2GraphRAG: Streamlining Graph-based RAG for High Efficiency and Effectiveness
Graph-based RAG methods like GraphRAG have shown promising global understanding of the knowledge base by constructing hierarchical entity graphs. However, they often suffer from inefficiency and rely on manually pre-defined query modes, limiting practical use. In this paper, we propose E^2GraphRAG, a streamlined graph-based RAG framework that improves both Efficiency and Effectiveness. During the indexing stage, E^2GraphRAG constructs a summary tree with large language models and an entity grap…

Towards Understanding Bias in Synthetic Data for Evaluation
Hossein A. Rahmani, Varsha Ramineni, Nick Craswell, Bhaskar Mitra, Emine Yilmaz
https://arxiv.org/abs/2506.10301

Towards Understanding Bias in Synthetic Data for Evaluation
Test collections are crucial for evaluating Information Retrieval (IR) systems. Creating a diverse set of user queries for these collections can be challenging, and obtaining relevance judgments, which indicate how well retrieved documents match a query, is often costly and resource-intensive. Recently, generating synthetic datasets using Large Language Models (LLMs) has gained attention in various applications. While previous work has used LLMs to generate synthetic queries or documents to imp…

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Robin Geens, Marian Verhelst
https://arxiv.org/abs/2506.02523 https://

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, improves the efficiency of large language models by projecting query, key, and value tensors into a compact latent space. This architectural change reduces the KV-cache size and significantly lowers memory bandwidth demands, particularly in the autoregressive decode phase. This letter presents the first hardware-centric analysis of MLA, comparing it to conventional Multi-Head Attention (MHA) and evaluating its implications for accele…

NetPress: Dynamically Generated LLM Benchmarks for Network Applications
Yajie Zhou, Jiajun Ruan, Eric S. Wang, Sadjad Fouladi, Francis Y. Yan, Kevin Hsieh, Zaoxing Liu
https://arxiv.org/abs/2506.03231

NetPress: Dynamically Generated LLM Benchmarks for Network Applications
Despite growing interest in domain-specific benchmarking of large language models (LLMs) and agents, current evaluations remain limited to static, small-scale datasets, especially in high-stakes tasks like network operations that demand reliability for deployments. We present NetPress, an automated benchmark generation framework for evaluating LLM agents in network applications. NetPress introduces a unified abstraction with state and action, enabling dynamic generation of diverse query sets al…

GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval
Lingyuan Liu, Mengxiang Zhang
https://arxiv.org/abs/2506.04762

GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval
Large language models (LLMs)-based query expansion for information retrieval augments queries with generated hypothetical documents with LLMs. However, its performance relies heavily on the scale of the language models (LMs), necessitating larger, more advanced LLMs. This approach is costly, computationally intensive, and often has limited accessibility. To address these limitations, we introduce GOLFer - Smaller LMs-Generated Documents Hallucination Filter & Combiner - a novel method leveragin…

TailorSQL: An NL2SQL System Tailored to Your Query Workload
Kapil Vaidya, Jialin Ding, Sebastian Kosak, David Kernert, Chuan Lei, Xiao Qin, Abhinav Tripathy, Ramesh Balan, Balakrishnan Narayanaswamy, Tim Kraska
https://arxiv.org/abs/2505.23039

TailorSQL: An NL2SQL System Tailored to Your Query Workload
NL2SQL (natural language to SQL) translates natural language questions into SQL queries, thereby making structured data accessible to non-technical users, serving as the foundation for intelligent data applications. State-of-the-art NL2SQL techniques typically perform translation by retrieving database-specific information, such as the database schema, and invoking a pre-trained large language model (LLM) using the question and retrieved information to generate the SQL query. However, existin…

Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma, Min Zhang
https://arxiv.org/abs/2506.01668

Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach
Stickers, though small, are a highly condensed form of visual expression, ubiquitous across messaging platforms and embraced by diverse cultures, genders, and age groups. Despite their popularity, sticker retrieval remains an underexplored task due to the significant human effort and subjectivity involved in constructing high-quality sticker query datasets. Although large language models (LLMs) excel at general NLP tasks, they falter when confronted with the nuanced, intangible, and highly spec…

This https://arxiv.org/abs/2409.17275 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents from a corpus and integrates them into the LLMs' generation process. In this study, we investigate the adversarial robustness of RAG, focusing specifically on examining the retrieval system. First, across 225 different setup combinations of corpus,…

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
Krishna Acharya, Aleksandr V. Petrov, Juba Ziani
https://arxiv.org/abs/2506.01910

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for sequential recommendation. Unlike prior methods such as GPT4Rec, which rely on lexical matching via BM25, GLoSS uses semantic search to retrieve relevant items beyond lexical matching. For query generation, we employ 4-bit quantized LlaMA-3 models fine-tuned with low-rank adaptation (LoRA), enabling efficient training and …

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Josefa Lia Stoisser, Marc Boubnovski Martell, Kaspar M\"artens, Lawrence Phillips, Stephen Michael Town, Rory Donovan-Maiye, Julien Fauqueur
https://arxiv.org/abs/2505.21801

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

This https://arxiv.org/abs/2503.18941 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Exploring Training and Inference Scaling Laws in Generative Retrieval
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel paradigm, the mechanisms that underpin its performance and scalability remain largely unexplored. We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance. We propose a novel evaluatio…

KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W. Lee, Sangdoo Yun, Hyun Oh Song
https://arxiv.org/abs/2505.23416

KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Transformer-based large language models (LLMs) cache context as key-value (KV) pairs during inference. As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency. This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries. KVzip quantifies the importance of a KV pair using the underlying LLM to reconstruct original contexts from cached KV pairs, subsequ…

This https://arxiv.org/abs/2505.21801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

This https://arxiv.org/abs/2505.07155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Reassessing Large Language Model Boolean Query Generation for Systematic Reviews
Systematic reviews are comprehensive literature reviews that address highly focused research questions and represent the highest form of evidence in medicine. A critical step in this process is the development of complex Boolean queries to retrieve relevant literature. Given the difficulty of manually constructing these queries, recent efforts have explored Large Language Models (LLMs) to assist in their formulation. One of the first studies,Wang et al., investigated ChatGPT for this task, foll…

This https://arxiv.org/abs/2412.00639 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries
Multi-modal datasets, like those involving images, often miss the detailed descriptions that properly capture the rich information encoded in each item. This makes answering complex natural language queries a major challenge in this domain. In particular, unlike the traditional nearest neighbor search, where the tuples and the query are represented as points in a single metric space, these settings involve queries and tuples embedded in fundamentally different spaces, making the traditional que…

This https://arxiv.org/abs/2505.19988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Automatic Metadata Extraction for Text-to-SQL
Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These experiences inform the direction of our research. Text-to-SQL benchmarks such as SPIDER and Bird contain extensive metadata that is gener…

This https://arxiv.org/abs/2502.17057 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the retriever as separate modules, overlooking the alignment between generation and ranking objectives. In this work, we propose ExpandR, a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. ExpandR employs the LLM to generate semantically rich query expansions, which are…

This https://arxiv.org/abs/2503.00600 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) -- a declarative abstraction for specifying and …

Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
Seongwan Park, Taeklim Kim, Youngjoong Ko
https://arxiv.org/abs/2506.00041

Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
Despite their strong performance, Dense Passage Retrieval (DPR) models suffer from a lack of interpretability. In this work, we propose a novel interpretability framework that leverages Sparse Autoencoders (SAEs) to decompose previously uninterpretable dense embeddings from DPR models into distinct, interpretable latent concepts. We generate natural language descriptions for each latent concept, enabling human interpretations of both the dense embeddings and the query-document similarity scores…