Ksenia Se

Kseniase

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8 New Types of RAG RAG techniques continuously evolve to enhance LLM response accuracy by retrieving relevant external data during generation. To keep up with current AI trends, new RAG types incorporate deep step-by-step reasoning, tree search, citations, multimodality and other effective techniques. Here's a list of 8 latest RAG advancements: 1. DeepRAG -> https://huggingface.co/papers/2502.01142 Models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic retrieval. It dynamically decides when to retrieve external knowledge and when rely on parametric reasoning. 2. RealRAG -> https://huggingface.co/papers/2502.00848 Enhances  novel object generation by retrieving real-world images and using self-reflective contrastive learning to fill knowledge gap, improve realism and reduce distortions. 3. Chain-of-Retrieval Augmented Generation (CoRAG) -> https://huggingface.co/papers/2501.14342 Retrieves information step-by-step and adjusts it, also deciding how much compute power to use at test time. If needed it reformulates queries. 4. VideoRAG -> https://huggingface.co/papers/2501.05874 Enables unlimited-length video processing, using dual-channel architecture that integrates graph-based textual grounding and multi-modal context encoding. 5. CFT-RAG ->  https://huggingface.co/papers/2501.15098 A tree-RAG acceleration method uses an improved Cuckoo Filter to optimize entity localization, enabling faster retrieval. 6. Contextualized Graph RAG (CG-RAG) -> https://huggingface.co/papers/2501.15067 Uses Lexical-Semantic Graph Retrieval (LeSeGR) to integrate sparse and dense signals within graph structure and capture citation relationships 7. GFM-RAG -> https://huggingface.co/papers/2502.01113 A graph foundation model that uses a graph neural network to refine query-knowledge connections 8. URAG -> https://huggingface.co/papers/2501.16276 A hybrid system combining rule-based and RAG methods to improve lightweight LLMs for educational chatbots
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Other important RAG advancements:

  • SafeRAG (a benchmark) -> https://huggingface.co/papers/2501.18636
    Establishes a security benchmark revealing how RAG systems are vulnerable to attacks like adversarial data injection, inter-context conflicts, and soft ad poisoning. Evaluates weaknesses in 14 RAG components, emphasizing the need for better filtering and security measures.

  • Topic-FlipRAG: Adversarial Opinion Manipulation -> https://huggingface.co/papers/2502.01386
    Demonstrates a two-stage adversarial attack that manipulates RAG-generated opinions on sensitive topics. Alters retrieval rankings and LLM reasoning to subtly flip the stance of generated answers, exposing the difficulty of mitigating semantic-level manipulation.

  • Experiments with LLMs on RAG for Closed-Source Simulation Software -> https://huggingface.co/papers/2502.03916
    Tests how RAG can support proprietary software by injecting relevant documentation dynamically. Shows that retrieval helps mitigate hallucinations in closed-source contexts, though some knowledge gaps remain, necessitating further improvements.

  • Health-RAG -> https://huggingface.co/papers/2502.04666
    Focuses on medical information retrieval by introducing a three-stage pipeline: retrieve, generate a reference summary (GenText), and re-rank based on factual alignment. Ensures accurate, evidence-backed health answers while mitigating misinformation risks.

upvoted 10 papers 1 day ago
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8 New Types of RAG

RAG techniques continuously evolve to enhance LLM response accuracy by retrieving relevant external data during generation. To keep up with current AI trends, new RAG types incorporate deep step-by-step reasoning, tree search, citations, multimodality and other effective techniques.

Here's a list of 8 latest RAG advancements:

1. DeepRAG -> DeepRAG: Thinking to Retrieval Step by Step for Large Language Models (2502.01142)
Models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic retrieval. It dynamically decides when to retrieve external knowledge and when rely on parametric reasoning.

2. RealRAG -> RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning (2502.00848)
Enhances  novel object generation by retrieving real-world images and using self-reflective contrastive learning to fill knowledge gap, improve realism and reduce distortions.

3. Chain-of-Retrieval Augmented Generation (CoRAG) -> Chain-of-Retrieval Augmented Generation (2501.14342)
Retrieves information step-by-step and adjusts it, also deciding how much compute power to use at test time. If needed it reformulates queries.

4. VideoRAG -> VideoRAG: Retrieval-Augmented Generation over Video Corpus (2501.05874)
Enables unlimited-length video processing, using dual-channel architecture that integrates graph-based textual grounding and multi-modal context encoding.

5. CFT-RAG ->  CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter (2501.15098)
A tree-RAG acceleration method uses an improved Cuckoo Filter to optimize entity localization, enabling faster retrieval.

6. Contextualized Graph RAG (CG-RAG) -> CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs (2501.15067)
Uses Lexical-Semantic Graph Retrieval (LeSeGR) to integrate sparse and dense signals within graph structure and capture citation relationships

7. GFM-RAG -> GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation (2502.01113)
A graph foundation model that uses a graph neural network to refine query-knowledge connections

8. URAG -> URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT (2501.16276)
A hybrid system combining rule-based and RAG methods to improve lightweight LLMs for educational chatbots
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What is test-time compute and how to scale it?

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🌁#86: Four Freedoms of truly open AI

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