google/gemma-3-27b-it
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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
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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Nebo-T1-Russian
(Probably) the first "longCoT" dataset for the Russian language created via Deeseek-R1.
- Prompts taken from the Sky-T1 dataset and translated via Llama3.3-70B.
- Answers and reasoning generated by Deepseek-R1 (685B).
- 16.4K samples in total, ≈12.4K Russian-only (in the rest, either the answer or reasoning is in English).
- Languages in the answers and reasoning are labeled using fasttext.
kristaller486/Nebo-T1-Russian
(Probably) the first "longCoT" dataset for the Russian language created via Deeseek-R1.
- Prompts taken from the Sky-T1 dataset and translated via Llama3.3-70B.
- Answers and reasoning generated by Deepseek-R1 (685B).
- 16.4K samples in total, ≈12.4K Russian-only (in the rest, either the answer or reasoning is in English).
- Languages in the answers and reasoning are labeled using fasttext.
kristaller486/Nebo-T1-Russian
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Wrong format?
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#1 opened about 2 months ago
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AlexPoto
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