Papers with retrieval phase
QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for large-scale query-document retrieval are expensive and require sparse handcrafted features. |
| Approach: | They propose a quadrupletBERT model for effective and efficient retrieval using pre-trained language models like BERT. |
| Outcome: | The proposed model improves retrieval phase and leverages distances between simple negative and hard negative instances to obtain better embeddings. |
CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language model agents rely on external memory to support knowledge reuse and reasoning tasks. |
| Approach: | They propose a CLustering-based AGentic memory framework where an agent actively organizes memory . they employ an SLM-agent driven router to assign each new memory to a semantically coherent cluster . |
| Outcome: | The proposed framework improves answer quality and robustness over previous memory systems. |
KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods that address corpus-level context loss focus on query enrichment through structured relation representations. |
| Approach: | They propose a framework for Contextual Query Retrieval that enriches queries with contextual representations derived from a corpus-centric KG. |
| Outcome: | The proposed framework outperforms strong baselines on RAGBench and MultiHop-RAG datasets in terms of retrieval effectiveness. |
Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Open-domain question answering (ODQA) systems typically adopt a retriever-reader architecture, where the retriever finds relevant documents, and the reader extracts or synthesizes answers. |
| Approach: | They propose a method that iteratively adjusts the importance weights of QE terms based on their relevance, refining term distinction and enhancing the separation of relevant terms. |
| Outcome: | The proposed method improves retrieval accuracy and overall performance on four ODQA datasets and five QE methods. |
MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing RAG solutions for large language models are limited by context windows limiting their ability to process long-form, domain-specific content. |
| Approach: | They propose a multimodal knowledge graph-based RAG that enables cross-modal reasoning . their method incorporates visual cues into the construction of knowledge graphs, retrieval phase, and answer generation process . |
| Outcome: | Experimental results show that the proposed approach outperforms existing approaches on textual and multimodal benchmarks. |