Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL queries. We construct a Deep Contextual Schema Link Graph, which contains key information and semantic relationship between a question and its database schema items. This graph-based structure enables effective representation of Text-to-SQL samples and retrieval of useful demonstrations for in-context learning. Experimental results on the Spider benchmark demonstrate the effectiveness of our approach, showing consistent improvements in SQL generation performance and efficiency across both hyper-scaled LLMs and small LLMs. The code is available at https://github.com/jjklle/DCG-SQL.

Large Language Models (LLM) have achieved remarkable performances in general domains and are now extending into the expert domain of law.Several benchmarks have been proposed to evaluate LLMs' legal capabilities.However, these benchmarks fail to evaluate open-ended and provisiongrounded Question Answering (QA).To address this, we introduce a Korean Benchmark for Legal EXplainable QA (KOBLEX), designed to evaluate provision-grounded, multihop legal reasoning.KOBLEX includes 226 scenario-based QA instances and their supporting provisions, created using a hybrid LLM-human expert pipeline.We also propose a method called Parametric provisionguided Selection Retrieval (PARSER), which uses LLM-generated parametric provisions to guide legally grounded and reliable answers.PARSER facilitates multi-hop reasoning on complex legal questions by generating parametric provisions and employing a three-stage sequential retrieval process.Furthermore, to better evaluate the legal fidelity of the generated answers, we propose Legal Fidelity Evaluation (LF-EVAL).LF-EVAL is an automatic metric that jointly considers the question, answer, and supporting provisions and shows a high correlation with human judgments.Experimental results show that PARSER consistently outperforms strong baselines, achieving the best results across multiple LLMs.Notably, compared to standard retrieval with GPT-4o, PARSER achieves 37.91 higher F-1 and 30.81 higher LF-EVAL.Further analyses reveal that PARSER efficiently delivers consistent performance across reasoning depths, with ablations confirming the effectiveness of PARSER. 1 * Equal Contribution. 1 The code and dataset are available at https://github. com/daehuikim/

As high-speed networks are becoming commonplace, it is increasingly challenging to prevent the attack attempts at the edge of the Internet. While many high-performance intrusion detection systems (IDSes) employ dedicated network processors or special memory to meet the demanding performance requirements, it often increases the cost and limits functional flexibility. In contrast, existing software-based IDS stacks fail to achieve a high throughput despite modern hardware innovations such as multicore CPUs, manycore GPUs, and 10 Gbps network cards that support multiple hardware queues.