Papers with retrieval training
Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval (2026.acl-long)
Copied to clipboard
Joaquin Polonuer, Lucas Vittor, Iñaki Arango, Ayush Noori, David A. Clifton, Luciano Del Corro, Marinka Zitnik
| Challenge: | ARK: Adaptive Retriever of Knowledge is a tool-using KG retriever that allows a language model to control breadth-depth tradeoffs without requiring a fragile seed selection or pre-set hop depth. |
| Approach: | They propose a tool-using KG retriever that gives a language model control over breadth-depth tradeoff using global lexical search over node descriptors and one-hop neighborhood exploration that composes into multi-hop traversal. |
| Outcome: | The proposed model improves on a teacher's dataset by +7.0, +26.6, and +13.5% while retaining 98.5% of the teacher' s Hit@1 rate. |
Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge-Aware Visual Question Answering about Entity tasks require two separate steps to generate accurate answers. |
| Approach: | They propose a multi-level information RAG approach that enhances answer generation through entity retrieval and query expansion. |
| Outcome: | The proposed approach improves answer generation through entity retrieval and query expansion. |
TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for identifying adversarial techniques in security texts face a trade-off: generic models with limited domain precision or resource-intensive pipelines. |
| Approach: | They propose a domain-specific retrieval-augmented generation framework that integrates off-the-shelf retrievers, instruction-tuned LLMs, and minimal text–technique pairs. |
| Outcome: | The proposed framework improves retrieval quality and domain specificity without extensive optimizations. |