{"total":13,"rows":[{"id":"2409.14924","title":"Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely","created":"2024-09-23","cited_by_count":66,"area":"rag"},{"id":"2407.10930","title":"Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together","created":"2024-07-15","cited_by_count":40,"area":"rag"},{"id":"2407.03227","title":"Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning","created":"2024-07-03","cited_by_count":16,"area":"rag"},{"id":"2410.01066","title":"From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems","created":"2024-10-01","cited_by_count":16,"area":"rag"},{"id":"2307.05074","title":"Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain","created":"2023-07-11","cited_by_count":12,"area":"rag"},{"id":"2404.12560","title":"Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL","created":"2024-04-19","cited_by_count":12,"area":"rag"},{"id":"2403.09226","title":"Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records","created":"2024-03-14","cited_by_count":8,"area":"rag"},{"id":"2501.09928","title":"Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs","created":"2025-01-17","cited_by_count":7,"area":"rag"},{"id":"2502.21107","title":"Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation","created":"2025-02-28","cited_by_count":4,"area":"rag"},{"id":"2608.21375","title":"SchemaRouter: Field-Aware Tool Routing for Efficient Heterogeneous Agentic RAG","created":"2026-07-03","cited_by_count":2,"area":"rag"},{"id":"2603.09704","title":"Evaluation of LLMs in retrieving food and nutritional context for RAG systems","created":"2026-03-10","cited_by_count":1,"area":"rag"},{"id":"2606.09724","title":"Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain","created":"2026-06-08","cited_by_count":0,"area":"rag"},{"id":"2608.22651","title":"Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation","created":"2026-08-23","cited_by_count":0,"area":"rag"}],"next_cursor":null}