Even when grounded in structured knowledge, large language models can still generate factually incorrect information – this research explores why and offers a new approach to detecting these ‘hallucinations’.
A new approach uses reinforcement learning to significantly reduce factual errors and improve the consistency of answers from large language models, across both quick queries and in-depth explanations.
A new approach combines reinforcement learning and equilibrium concepts to optimize investment portfolios even when faced with sudden, unpredictable market shifts.
A new analysis assesses how well artificial intelligence tools can automatically pull crucial data from the ever-growing body of materials science research.
Despite impressive gains in artificial intelligence, new research reveals that even the most advanced language models struggle with fundamental biases and strategic inconsistencies during complex negotiations.
Researchers have developed a novel framework, BugSweeper, that leverages graph neural networks to pinpoint vulnerabilities within smart contract code with greater precision.
Researchers are harnessing the power of machine learning to isolate the faint signals from the universe’s first stars and galaxies.
Researchers have demonstrated a novel method for deceiving AI-powered image detectors, highlighting critical vulnerabilities in current authentication systems.
A new analysis frames the strengths and weaknesses of transformer models through the principles of graph neural networks, shedding light on their internal workings.
Researchers are building artificial intelligence agents powered by large language models and external knowledge to deliver more accurate, consistent, and transparent financial decisions.