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Dmitri Kalaev

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Noah Fatsi

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Daniel Barcklow

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References (31)

[1]
Influence Guided Context Selection for Effective Retrieval-Augmented Generation
2025Jiale Deng, Yanyan Shen et al.
[2]
Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models
2025Igor Halperin
[3]
An Information-Theoretic Framework for Retrieval-Augmented Generation Systems
2025Semih Yumuşak
[4]
A Comparative Study of Specialized LLMs as Dense Retrievers
2025Hengran Zhang, Keping Bi et al.
[5]
RePCS: Diagnosing Data Memorization in LLM-Powered Retrieval-Augmented Generation
2025Le Vu Anh, Nguyen Viet Anh et al.
[6]
ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
2025Ruofan Wu, Youngwon Lee et al.
[7]
CReSt: A Comprehensive Benchmark for Retrieval-Augmented Generation with Complex Reasoning over Structured Documents
2025Minsoo Khang, Sangjun Park et al.
[8]
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation
2025Ruizhe Li, Chen Chen et al.
[9]
HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations
2025Peiru Yang, Xintian Li et al.
[10]
LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
2025Zhuocheng Zhang, Yang Feng et al.
[11]
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
2025Bernal Jim'enez Guti'errez, Yiheng Shu et al.
[12]
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs
2025M. R. Rezaei, Adji Bousso Dieng
[13]
OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
2025Shuting Wang, Jiejun Tan et al.
[14]
Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation
2024Tianyu Liu, Jirui Qi et al.
[15]
LightRAG: Simple and Fast Retrieval-Augmented Generation
2024Zirui Guo, Lianghao Xia et al.
[16]
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
2024Jimmy Lin
[17]
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
2024Rishi Kalra, Zekun Wu et al.
[18]
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs
2024Wei-Chau Xie, Xuefeng Liang et al.
[19]
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction
2024Bhaskarjit Sarmah, Dhagash Mehta et al.
[20]
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
2024R. Friel, Masha Belyi et al.

Showing 20 of 31 references

Founder's Pitch

"MIGRASCOPE offers a revolutionary toolkit for benchmarking and optimizing retrievers in RAG systems using information theory."

RAGScore: 5View PDF ↗

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High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

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4/4 signals

10

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Why It Matters

This research establishes a novel information-theoretic framework for evaluating retrievers in RAG systems, which are critical for improving the efficiency and accuracy of large language models by providing relevant context.

Product Angle

The framework can be integrated into existing NLP pipelines as a tool or API, providing insights and recommendations on retriever configurations to improve system performance.

Disruption

MIGRASCOPE could replace existing retrieval benchmarking systems by offering a more nuanced and data-driven evaluation approach, improving the selection and combination of retrievers.

Product Opportunity

As the demand for accurate information retrieval in AI systems grows, companies working with large datasets will pay for tools that optimize retrieval efficiency and relevance, representing a significant market opportunity.

Use Case Idea

Develop a SaaS platform that utilizes MIGRASCOPE to help businesses optimize retriever settings in their NLP systems, enhancing search relevance and retrieval efficiency.

Science

The paper introduces MIGRASCOPE, an information-theoretic framework that evaluates retriever quality using mutual information to analyze retriever overlaps and their individual contributions within RAG systems.

Method & Eval

The method uses mutual information to assess retriever performance and evaluate redundancy and synergy among retrievers across various datasets, showing superior results with ensemble retrievers versus single ones.

Caveats

The framework relies heavily on accurate estimation of mutual information and may require significant computational resources for large datasets. It may also face challenges in adoption due to existing system inertia.

Author Intelligence

Wenqing Zheng

LEAD
Capital One
wenqing.zheng@capitalone.com

Dmitri Kalaev

Capital One

Noah Fatsi

Capital One

Daniel Barcklow

Capital One

Owen Reinert

Capital One

Igor Melnyk

Capital One

Senthil Kumar

Capital One

C. Bayan Bruss

Capital One