Method Drift›Retrieval-augmented generation
BM25
Retrieval-augmented generation
superseded — cited as a baseline and beaten by newer methods
6 papers critique it · 16 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites BM25 as a baseline.
“This paradigm inevitably discards critical visual and structural information, including the row-column relationships of tables, data trends of charts, and layout logic between text and figures.”
— LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding“The semantic and lexical gaps limit the effectiveness of sparse models like BM25 and TF-IDF, which operate on keyword matching”
— ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever“Despite their widespread use, many RAG systems rely on static, off-the-shelf retrieval modules — e.g., BM25 ... that are minimally adapted to the downstream task or domain.”
— Test-time Corpus Feedback: From Retrieval to RAG“We prioritized a dense retrieval approach over sparse methods (such as BM25) because layperson queries often lack the precise legal terminology found in regulatory documents.”
— Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation“They offer computational efficiency but lack deeper semantic comprehension.”
— MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation“However, many practical systems still assume that one fixed retrieval strategy is sufficient across all tasks. This assumption is problematic in realistic agent settings.”
— An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration
Beaten on benchmarks
Head-to-head results where a newer method reports beating BM25. Values are copied from the source paper's tables — verify against the cited paper.
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · Hit@1 [STARK-PRIME]
63.71 vs 12.75
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · Hit@5 [STARK-PRIME]
75.39 vs 27.92
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · R@20 [STARK-PRIME]
76.39 vs 31.25
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · MRR [STARK-PRIME]
68.99 vs 19.84
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · Hit@1 [STARK-MAG]
69.64 vs 25.85
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · Hit@5 [STARK-MAG]
84.32 vs 45.25
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · R@20 [STARK-MAG]
89.12 vs 45.69
- GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
GraphRAFT beats BM25 · MRR [STARK-MAG]
76.24 vs 34.91
- A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
QMKGF beats BM25 · R-1 [HotpotQA]
64.98 vs 54.86
- A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
QMKGF beats BM25 · R-1 [MuSiQue]
47.42 vs 38.53
- Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
PURPLE beats BM25 · ROUGE-1 [Phi-4-Mini-Instruct (3.84B)]
26.2 vs 25.2
- Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Corpus2Skill beats BM25 · F1 [WixQA benchmark]
0.456 vs 0.345
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- Beyond Topical SimilarityBeyond Topical Similarity: Contrastive Evidence Retrieval with Interpretable Attention Alignment in RAGMay 31, 2026
- Experience-RAG SkillAn Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy OrchestrationMay 5, 2026
- LFRAGLFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document UnderstandingApr 18, 2026
- Don't Retrieve, NavigateDon't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAGApr 16, 2026
- Feb 25, 2026
- SEAL-RAGReplace, Don't Expand: Mitigating Context Dilution in Multi-Hop RAG via Fixed-Budget Evidence AssemblyDec 11, 2025
- ModernBERT + ColBERTModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retrieverOct 6, 2025
- Cluster-based Adaptive Retrieval (CAR)Cluster-based Adaptive Retrieval: Dynamic Context Selection for RAG ApplicationsOct 2, 2025