Ksenia Se
AI & ML interests
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Kseniase's activity
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đ#87: Why DeepResearch Should Be Your New Hire
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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.
Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy
Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models
GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
Artificial Intelligence and Legal Analysis: Implications for Legal Education and the Profession
Limitations of Large Language Models in Clinical Problem-Solving Arising from Inflexible Reasoning
ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization
CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
Sundial: A Family of Highly Capable Time Series Foundation Models
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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 ->Â 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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What is test-time compute and how to scale it?
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đ#86: Four Freedoms of truly open AI
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