This paper by Orgad et al. delves into the internal workings of Large Language Models (LLMs) to understand how errors, often termed "hallucinations," are represented. Moving beyond purely external, behavioral analyses, the authors use probing techniq...
That's a wonderfully insightful connection to draw! The Three-Body Problem, at its heart, deals with the inherent unpredictability and complex dynamics that arise from the gravitational interactions of just three celestial bodies. Correlating it with...
So while I was working on some NLP side projects, I had this thought — YouTube videos are packed with great information, but most of the time they’re way too long. Even if someone’s explaining something valuable, it takes 15-20 minutes to get to the ...
This paper by David Silver and Richard Sutton, prominent figures in Reinforcement Learning (RL), proposes a transition to an "Era of Experience" where AI agents, learning autonomously from world interaction, will achieve "superhuman capabilities." Wh...
Artificial Intelligence often greets us with a human-like voice, engaging in conversations that can feel surprisingly natural, even empathetic. This powerful mimicry tempts us to project human qualities – understanding, intent, consciousness – onto t...
Anthropic's paper investigating the "faithfulness" of Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) enters a critical discourse on AI capabilities and safety. CoT is often presented as offering transparency into model processing. T...
Lujain Ibrahim and Myra Cheng's position paper addresses a critical and growing trend in Artificial Intelligence: the pervasive use of human characteristics and analogies – anthropomorphism – to describe and develop Large Language Models (LLMs). The ...
Imagine you're working to build AI that meaningfully changes lives—helping students learn, assisting developers with code, or enabling everyday people to navigate a complex world. Today's AI research landscape offers impressive benchmark results and ...
The field of Artificial Intelligence, particularly Large Language Models (LLMs), is experiencing a gold rush. Papers flood conferences, benchmarks fall weekly, and the hype machine runs at full throttle. Yet amid this frenzy, we've abandoned the very...
Introduction Despite the remarkable success of deep learning models, particularly large neural networks and pre-trained language models (LLMs), significant challenges arise when applying these architectures to program synthesis and abstract reasoning...
