Papers with R**etrieval
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Large language models struggle to evaluate the correctness of non-parametric knowledge when it differs from internal memorization, leading to knowledge conflicts during response generation. |
| Approach: | They propose a lightweight alignment method to leverage multi-source knowledge based on retrieval relevance. |
| Outcome: | Experiments on four datasets show that the proposed method outperforms RAG by 4-10% in accuracy without any extra component. |
DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | RAG systems leave out important relevant information (low recall) and excessively related but irrelevant information (high precision) authors propose a manual annotation-free schema that can be used for RAGs with limited performance. |
| Approach: | They propose a manual annotation-free schema that annotates unseen queries with calibrated relevance scores. |
| Outcome: | Evaluators show that DIRAS can achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs. |
FIER: Fine-Grained and Efficient KV Cache Retrieval for Long-context LLM Inference (2025.findings-emnlp)
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Dongwei Wang, Zijie Liu, Song Wang, Yuxin Ren, Jianing Deng, Jingtong Hu, Tianlong Chen, Huanrui Yang
| Challenge: | Key-Value (KV) cache reading latency increases with context lengths hindering LLM inference . important tokens are sparsely distributed across the long context, making existing retrieval inaccurate . |
| Approach: | They propose a method to retain a small fraction of KV cache based on token importance . important tokens are often sparsely distributed across the long context . |
| Outcome: | The proposed method reduces decoding latency by 1.2 to 1.5. |
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to augment large language models with external documents are lacking in the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
| Approach: | They propose to integrate R2AG into R2etrieval augmented generation framework by using a R2-Former to capture retrieval information. |
| Outcome: | The proposed framework fills the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
History-Aware Conversational Dense Retrieval (2024.findings-acl)
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| Challenge: | Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy. |
| Approach: | They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns. |
| Outcome: | The proposed system improves on two public conversational search datasets. |
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
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| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
ET-MIER: Entity Type-guided Key Mention Identification and Evidence Retrieval for Document-level Relation Extraction (2025.findings-emnlp)
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| Challenge: | Existing work does not fully distinguish the contribution of different mentions to entity representation and the importance of mentions in evidence sentences. |
| Approach: | They propose a document-level relation extraction task that uses entity mentions to identify relations between entities in a text. |
| Outcome: | The proposed model achieves state-of-the-art on widely-adopted datasets. |