Factuality - Edgentiq

SynthKGQA: Generating Better Data for Knowledge Graph-Augmented Language Models

Enhancing Vision-Language Models: A Watermark-Guided Approach to Reduce Hallucinations

GraphMERT: Building Factual and Scalable Knowledge Graphs for Domain-Specific AI

Evaluating AI’s Research Prowess: A New Framework for Assessing DeepResearch Systems

Probing LLM Misbehaviors with Positional Encoding Perturbations

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PruneCD: Enhancing LLM Factuality Through Intelligent Layer Pruning

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AI Agents That Understand Their Own Limits in Complex Data

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Empowering Everyday Individuals: A New Dataset for Chinese Legal Claim Generation

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Ensuring AI Text Completeness: The LongRecall Framework

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Evaluating AI’s Interpretive Accuracy in Customer Service Conversations

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Human Touch in AI Evaluation: Introducing the FACTORY Dataset

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OneShield: IBM’s Advanced Guardrail System for Large Language Models

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Enhancing AI’s Understanding of Real-World Locations

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PrismRAG: A New Approach to Enhance AI’s Factual Accuracy in Question Answering

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AutoRAG-LoRA: A New Approach to Combat AI Hallucinations with Adaptive Learning

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Gen AI News and Updates

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SynthKGQA: Generating Better Data for Knowledge Graph-Augmented Language Models

November 7, 2025

Enhancing Vision-Language Models: A Watermark-Guided Approach to Reduce Hallucinations

October 17, 2025

GraphMERT: Building Factual and Scalable Knowledge Graphs for Domain-Specific AI

October 13, 2025