Texonom
Texonom/
Science
Science/Mathematics/Math Field/Statistics/Statistical Model/Generative Model/
RAG
Loading views...
Search

RAG

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2023 Oct 25 11:31
Editor
Editor
Seonglae ChoSeonglae Cho
Edited
Edited
2026 Jun 26 15:7
Refs
Refs
Information retrieval
AI Retrieval
In-context Retrieval
Prompt Engineering Tool
Contextual Compression
Ranking Model

Retrieval-Augmented Generation

RAG is not Memory, Search or Memory is better than naive RAG. Better retrieval performance ≠ Better performance

Challenges

  • retrieval latency
  • system complexity
RAG Notion
Retrieval Accuracy
Modular RAG
Advanced RAG
Naive RAG
Graph RAG
RAG Caching
CAG
CRAG
HypaRAG
PixelRAG
https://arxiv.org/pdf/2309.01431.pdf
RAG Usages
RAG Framework
Embedding Transformation
Retrieve Augmented Learning
Sufficient Context Detection
RAG Interpretability
 
 
 
 
https://arxiv.org/pdf/2312.10997v1.pdf
https://arxiv.org/pdf/2312.10997v1.pdf

Beyond dot product

https://www.youtube.com/watch?v=mE7IDf2SmJg
 
 
 
 
Stanford CS25: V3 I Retrieval Augmented Language Models
December 5, 2023 Douwe Kiela, Contextual AI Language models have led to amazing progress, but they also have important shortcomings. One solution for many of these shortcomings is retrieval augmentation. I will introduce the topic, survey recent literature on retrieval augmented language models and finish with some of the main open questions. More about the course can be found here: https://web.stanford.edu/class/cs25/ View the entire CS25 Transformers United playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM
Stanford CS25: V3 I Retrieval Augmented Language Models
https://www.youtube.com/watch?v=mE7IDf2SmJg&list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM&index=25
Stanford CS25: V3 I Retrieval Augmented Language Models
Intro of Retrieval Augmented Generation (RAG) and application demos
Introduction of Retrieval Augmented Generation, Jupyter Notebook three demos of Basic RAG, Sentence-window retrieval, Auto-merging…
Intro of Retrieval Augmented Generation (RAG) and application demos
https://medium.com/@henryhengluo/intro-of-retrieval-augmented-generation-rag-and-application-demos-c1d9239ababf
Intro of Retrieval Augmented Generation (RAG) and application demos
NVIDIA Research: RAG with Long Context LLMs
This blog post dives into NVIDIA’s recent study comparing retrieval-augmentation with and without long-context LLMs.
NVIDIA Research: RAG with Long Context LLMs
https://blog.llamaindex.ai/nvidia-research-rag-with-long-context-llms-7d94d40090c4
NVIDIA Research: RAG with Long Context LLMs

KBQA,
Knowledge Graph

ML Blog - Improve ChatGPT with Knowledge Graphs
Leveraging knowledge graphs for LLMs using LangChain
https://mlabonne.github.io/blog/posts/Article_Improve_ChatGPT_with_Knowledge_Graphs.html
llama-recipes/recipes/use_cases/agents/langchain at main · meta-llama/llama-recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization an...
llama-recipes/recipes/use_cases/agents/langchain at main · meta-llama/llama-recipes
https://github.com/meta-llama/llama-recipes/tree/main/recipes/use_cases/agents/langchain
llama-recipes/recipes/use_cases/agents/langchain at main · meta-llama/llama-recipes
Base model is better at retrieval than
Instruction Tuning model
arxiv.org
https://arxiv.org/pdf/2406.14972
Passages retrieved through RAG search often have little relation to each other, resulting in attention forming a block-diagonal structure. Therefore, the argument is that feeding all tokens directly into the decoder is computationally wasteful.
arxiv.org
https://arxiv.org/pdf/2509.01092

Beyond RAG: Terminal is all you need

Beyond Semantic Similarity: Rethinking Retrieval for Agentic...
Modern retrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning. This...
Beyond Semantic Similarity: Rethinking Retrieval for Agentic...
https://arxiv.org/abs/2605.05242
Beyond Semantic Similarity: Rethinking Retrieval for Agentic...
Terminal Is All You Need: Design Properties for Human-AI Agent Collaboration
While research on AI agents focuses on enabling them to operate graphical user interfaces, the most effective and widely adopted agent tools in practice are terminal-based. We argue that this convergence is not coincidental. It reflects three design properties central to effective human-AI-UI collaboration: representational compatibility between agent and interface, transparency of agent actions within the interaction medium, and low barriers to entry for human participants. We ground each property in established HCI theory, show how terminal-based tools satisfy them by default, and argue that any modality, including graphical and spatial interfaces, must be deliberately engineered to achieve them. Rather than a legacy artifact, the terminal serves as a design exemplar whose properties any agent-facing modality must replicate.
Terminal Is All You Need: Design Properties for Human-AI Agent Collaboration
https://arxiv.org/html/2603.10664v1
 
 

Backlinks

AI AgentReversing TransformerLangChainMachine UnlearningIn-context learningGenerative ModelLanguage Model ContextPrompt Engineering

Recommendations

Texonom
Texonom/
Science
Science/Mathematics/Math Field/Statistics/Statistical Model/Generative Model/
RAG