KnowledgeDB.ai

Mar 06 2025 20 ep. 20 mins 1

KnowledgeDB.ai Podcast artwork

KnowledgeDB.ai is your go-to podcast for diving deep into the infrastructure that powers Generative AI. Each episode explores groundbreaking papers, insightful publications, and emerging technologies shaping the future of AI systems. From distributed computing and graph databases to hardware accelerators and model optimization, we decode the research behind the tech. Whether you're a developer, researcher, or just curious about the mechanics behind GenAI, KnowledgeDB.ai provides a blend of technical depth and practical insights to keep you informed and inspired. Tune in and stay ahead of the

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LLM Post-Training: Reinforcement Learning, Scaling, and Fine-Tuning

Mar 06 2025 53 mins

Ref: https://arxiv.org/abs/2502.21321This document provides a comprehensive survey of post-training methodologies for Large Language Models (LLMs), focusing on refining reasoning capabilities and aligning models with user preferences and ethical standards. It categorizes these methodologies into fine-tuning, reinforcement learning (RL), and test-time scaling, while exploring the ch

State of Play on LLM and RAG: Preparing your Knowledge Organization for Generative AI

Jan 29 2025 12 mins

https://graphwise.ai/resources/white-paper/knowledge-organization-llm-rag/ This Unisphere Research report, sponsored by Semantic Web Company, examines the current state of Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) adoption among 382 knowledge management executives. The study highlights the pervasive use of LLMs, particularly for content creation and

LEGO-GraphRAG: Modularizing Graph-based RAG for Design Space Exploration

Jan 28 2025 12 mins

https://arxiv.org/abs/2411.05844 This research paper introduces LEGO-GraphRAG, a modular framework for improving Retrieval-Augmented Generation (RAG) systems that use knowledge graphs. The framework systematically categorizes existing RAG techniques and facilitates the creation of new, more efficient and effective RAG instances. The authors conduct empirical studies, evaluat

Knowledge Graphs for Trustworthy LLM Question Answering

Jan 26 2025 35 mins

https://www.sciencedirect.com/science/article/pii/S1570826824000441 This pre-print research paper investigates the use of knowledge graphs to improve the accuracy and trustworthiness of Large Language Model (LLM)-powered question answering systems in enterprise settings. The authors argue that knowledge graphs provide a crucial framework for validating LLM-generated queries,

Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions

Jan 26 2025 17 mins

https://arxiv.org/abs/2501.06699 This research paper examines the interplay between large language models (LLMs), knowledge graphs (KGs), and search engines (SEs) in fulfilling user information needs. The authors analyze the strengths and weaknesses of each technology across various dimensions, including correctness, completeness, and freshness. A taxonomy of user informatio

Seven Failure Points in Retrieval Augmented Generation Systems

Jan 19 2025 32 mins

This research paper examines the challenges of building robust Retrieval Augmented Generation (RAG) systems, which combine information retrieval with large language models. The authors identify seven common failure points in RAG system design based on three case studies from diverse domains. Key findings highlight the importance of runtime validation and the iterative nature

A Retrieval-Augmented Generation Based Large Language Model Benchmarked on a Novel Dataset

Jan 18 2025 12 mins

Modular RAG: Optimizing LLMs for Indigenous Knowledge Preservation This research paper explores a Retrieval-Augmented Generation (RAG) framework for large language models (LLMs). The study uses a novel dataset of interviews with Amazon rainforest natives and biologists to assess the impact of different RAG components (base language models like GPT and Palm, similarity scori

A Survey on Large Language Models with some Insights on their Capabilities and Limitations

Jan 12 2025 21 mins

https://arxiv.org/abs/2501.04040 The paper explores the foundations, capabilities, and limitations of Large Language Models (LLMs). It examines various training methodologies (unsupervised, supervised, semi-supervised), data preprocessing techniques, and model adaptation strategies like instruction and alignment tuning. The analysis includes a review of prominent LLMs (BERT, T5, G

Large Concept Models: Training, Inference, and Applications

Jan 05 2025 20 mins

This research paper introduces Large Concept Models (LCMs), a novel approach to language modeling that operates on sentence embeddings instead of individual tokens. LCMs aim to mimic human-like abstract reasoning by processing higher-level semantic representations, improving long-form text generation and zero-shot cross-lingual performance. The authors explore various LCM architect

FLAVA: A Foundational Language And Vision Alignment Model

Dec 12 2024 12 mins

Ref: https://arxiv.org/abs/2112.04482 The document introduces FLAVA, a foundational vision and language model that excels in vision, language, and multimodal tasks. Unlike previous models often focusing on specific modalities or employing either contrastive or multi-modal approaches but not both, FLAVA uses a unified transformer architecture and a novel pretraining scheme. Th