Detecting, Evaluating, and Reducing Hallucinations

Detecting hallucinations involves distinguishing accurate outputs from those that deviate from factual or contextual grounding. One approach is consistency checking, where LLM outputs are evaluated against external knowledge bases to identify discrepancies. Manakul et al. (2023) propose SelfCheckGPT, a zero-resource method that uses the model’s internal consistency to detect

Chinese Startup Introduced New DeepSeek-R1-0528 Model Approaching Market Leaders with 87.5% Accuracy

Chinese startup DeepSeek announced DeepSeek-R1-0528 on 28 May 2025, delivering significant performance improvements in complex reasoning tasks and achieving near-parity capabilities with paid models OpenAI o3 and Google Gemini 2.5 Pro. The update increased accuracy on the AIME 2025 test from 70% to 87.5%, whilst improving coding performance

by poltextLAB AI journalist

Conceptual Contrasts Between Parroting and Hallucination in Language Models

Advancements in artificial intelligence (AI), particularly in natural language processing (NLP), highlight critical distinctions between parroting and hallucination in language models. Parroting refers to AI reproducing or mimicking patterns and phrases from training data without demonstrating understanding or creativity. Hallucination involves generating factually incorrect, implausible, or fabricated outputs, often diverging

Sakana AI Introduces the Continuous Thought Machine: A New AI Architecture Built on Synchronised Neurons

Tokyo-based Sakana AI, co-founded by former top Google AI scientists, unveiled the Continuous Thought Machine (CTM) on May 12, 2025. The CTM is the first artificial neural network that uses neuron synchronisation as its core reasoning mechanism, enabling AI to "think" through problems step-by-step. This represents a significant

by poltextLAB AI journalist

The Environmental Costs of Artificial Intelligence: A Growing Concern

The rapid integration of Artificial Intelligence (AI) into global economies has driven transformative advancements in sectors such as healthcare and agriculture. However, this technological revolution incurs significant environmental costs, particularly through substantial energy consumption and greenhouse gas (GHG) emissions. The carbon footprint of AI, stemming from energy-intensive processes like hardware