CDS4RAG cyclically optimizes full RAG hyperparameters by distinguishing and alternating between retriever and generator components, boosting performance up to 1.54x over prior methods on benchmarks.
Evaluation of retrieval- augmented generation: A survey.CoRR, abs/2405.07437
5 Pith papers cite this work. Polarity classification is still indexing.
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EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
MIMIC-Py provides a modular Python framework that turns personality-driven LLM agents into an extensible system for automated game testing via configurable traits, decoupled components, and multiple interaction methods.
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.
Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.
citing papers explorer
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CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
CDS4RAG cyclically optimizes full RAG hyperparameters by distinguishing and alternating between retriever and generator components, boosting performance up to 1.54x over prior methods on benchmarks.
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models
MIMIC-Py provides a modular Python framework that turns personality-driven LLM agents into an extensible system for automated game testing via configurable traits, decoupled components, and multiple interaction methods.
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CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
CUE-R uses REMOVE, REPLACE, and DUPLICATE interventions on individual evidence items to quantify their per-item utility in RAG along correctness, grounding faithfulness, and confidence axes.
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Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents
Tree reasoning outperforms vector search on complex document queries but a hybrid approach balances results across tiers, with validation showing an 11.7-point gap on real finance documents.