Knowledge-Aware Diverse Reranking for Cross-Source Question Answering
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Abstract (arXiv)
This paper presents Team Marikarp's solution for the SIGIR 2025 LiveRAG competition. The competition's evaluation set, automatically generated by DataMorgana from internet corpora, encompassed a wide range of target topics, question types, question formulations, audience types, and knowledge organization methods. It offered a fair evaluation of retrieving question-relevant supporting documents from a 15M documents subset of the FineWeb corpus. Our proposed knowledge-aware diverse reranking RAG pipeline achieved first place in the competition.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 25 Jun 2025.
Builds on
5 of 12 references.
References · 5
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
- 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
- 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
- 2025Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorganacited by 13
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
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- 2026Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking
- 2026SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
- 2025Question Decomposition for Retrieval-Augmented Generation
- 2025Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
- 2025TopClustRAG at SIGIR 2025 LiveRAG Challenge
- 2025Can we repurpose multiple-choice question-answering models to rerank retrieved documents? ↗
- 2025SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs
- 2025LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation