{"total":35,"rows":[{"id":"2407.11005","title":"RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems","created":"2024-06-25","cited_by_count":117,"area":"rag"},{"id":"2406.13629","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","created":"2024-06-19","cited_by_count":101,"area":"rag"},{"id":"2410.11414","title":"ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability","created":"2024-10-15","cited_by_count":101,"area":"rag"},{"id":"2502.17125","title":"LettuceDetect: A Hallucination Detection Framework for RAG Applications","created":"2025-02-24","cited_by_count":34,"area":"rag"},{"id":"2507.18910","title":"A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions","created":"2025-07-25","cited_by_count":31,"area":"rag"},{"id":"2409.09046","title":"HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications","created":"2024-08-29","cited_by_count":22,"area":"rag"},{"id":"2406.17186","title":"CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation","created":"2024-06-24","cited_by_count":18,"area":"rag"},{"id":"2408.15533","title":"LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation","created":"2024-08-28","cited_by_count":17,"area":"rag"},{"id":"2510.06999","title":"Towards Reliable Retrieval in RAG Systems for Large Legal Datasets","created":"2025-10-08","cited_by_count":12,"area":"rag"},{"id":"2510.13975","title":"Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems","created":"2025-10-15","cited_by_count":10,"area":"rag"},{"id":"2509.13930","title":"Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG","created":"2025-09-17","cited_by_count":7,"area":"rag"},{"id":"2510.03418","title":"LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents","created":"2025-10-03","cited_by_count":7,"area":"rag"},{"id":"2512.08892","title":"Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders","created":"2025-12-09","cited_by_count":7,"area":"rag"},{"id":"2505.02164","title":"Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use","created":"2025-05-04","cited_by_count":6,"area":"rag"},{"id":"2511.17908","title":"Principled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction","created":"2025-11-22","cited_by_count":6,"area":"rag"},{"id":"2601.05866","title":"FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG","created":"2026-01-09","cited_by_count":6,"area":"rag"},{"id":"2409.06595","title":"GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering","created":"2024-09-10","cited_by_count":5,"area":"rag"},{"id":"2504.01840","title":"LRAGE: Legal Retrieval Augmented Generation Evaluation Tool","created":"2025-04-02","cited_by_count":5,"area":"rag"},{"id":"2410.23902","title":"Responsible Retrieval Augmented Generation for Climate Decision Making from Documents","created":"2024-10-31","cited_by_count":4,"area":"rag"},{"id":"2601.20084","title":"IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation","created":"2026-01-27","cited_by_count":3,"area":"rag"},{"id":"2505.15916","title":"BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law","created":"2025-05-21","cited_by_count":2,"area":"rag"},{"id":"2605.21071","title":"Fine-grained Claim-level RAG Benchmark for Law","created":"2026-05-20","cited_by_count":2,"area":"rag"},{"id":"2406.06566","title":"Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin","created":"2024-06-03","cited_by_count":1,"area":"rag"},{"id":"2507.21753","title":"Towards a rigorous evaluation of RAG systems: the challenge of due diligence","created":"2025-07-29","cited_by_count":1,"area":"rag"},{"id":"2509.21324","title":"From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data","created":"2025-08-27","cited_by_count":1,"area":"rag"},{"id":"2511.01643","title":"A Graph-based RAG for Energy Efficiency Question Answering","created":"2025-11-03","cited_by_count":1,"area":"rag"},{"id":"2512.01659","title":"HalluGraph: Auditable Hallucination Detection for Legal RAG Systems via Knowledge Graph Alignment","created":"2025-12-01","cited_by_count":1,"area":"rag"},{"id":"2511.22858","title":"RAG System for Supporting Japanese Litigation Procedures: Faithful Response Generation Complying with Legal Norms","created":"2025-11-28","cited_by_count":0,"area":"rag"},{"id":"2603.03300","title":"Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys","created":"2026-02-07","cited_by_count":0,"area":"rag"},{"id":"2603.07825","title":"Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation","created":"2026-03-08","cited_by_count":0,"area":"rag"},{"id":"2603.20309","title":"BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs","created":"2026-03-19","cited_by_count":0,"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":"2606.18103","title":"HistoRAG: Embedding Historical Methodology in Retrieval-Augmented Generation Through Critical Technical Practice","created":"2026-06-16","cited_by_count":0,"area":"rag"},{"id":"2606.28358","title":"How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation","created":"2026-06-09","cited_by_count":0,"area":"rag"},{"id":"2609.27814","title":"LabourCrew: A Multi-Agent RAG Framework for Trustworthy Adversarial Deliberation and Statutory Reasoning over Labour Law","created":"2026-08-18","cited_by_count":0,"area":"rag"}],"next_cursor":null}