😵💫 Are LLM Hallucinations Inevitable?
From diffusion models to cognitive-like capabilities in large language models - here's what's new in AI.
Welcome to your weekly AI Fridays, where we spotlight the latest breakthroughs in tech and research!
Here’s what’s new:
🖼️ Tutorial on Diffusion Models for Imaging and Vision: A guide to diffusion models for image and vision applications.
💡 Can LLMs Generate Novel Research Ideas?: A study shows LLMs generate more novel research ideas than human experts, but with lower feasibility.
🤖 LLMs Will Always Hallucinate: Hallucinations are inevitable in LLMs, and we must adapt to them.
⚡ State and Action Factorization in Power Grids: A new algorithm enhances reinforcement learning for power grid control.
🧠 Cognitive-Like Capabilities in LLMs: New evidence suggests LLMs exhibit human-like cognitive abilities.
Tutorial on Diffusion Models for Imaging and Vision (🔗 Read the Paper)
This tutorial paper provides an overview of diffusion models, explaining their fundamental concepts and recent advancements in image and vision applications, while targeting students interested in research or practical implementation of these models.
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers (🔗 Read the Paper)
A large-scale study with over 100 NLP researchers found that LLM-generated research ideas were judged as more novel than human expert ideas, though slightly weaker on feasibility, providing the first statistically significant evidence of LLMs' potential in research ideation while identifying key challenges and proposing future work to validate these findings.
LLMs Will Always Hallucinate, and We Need to Live With This (🔗 Read the Paper)
Hallucinations in large language models are mathematically inevitable due to their fundamental structure, challenging the notion that they can be eliminated and suggesting we must adapt to their inherent presence in AI-generated content.
State and Action Factorization in Power Grids (🔗 Read the Paper)
This paper proposes a data-driven algorithm to factorize state and action spaces in power grid control, enabling more efficient distributed reinforcement learning by identifying correlated state-action pairs and creating simpler subproblems, with results validated on a Grid2Op simulator benchmark.
Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement? (🔗 Read the Paper)
This study reveals evidence of a general intelligence factor in large language models, similar to human cognitive abilities, and identifies a combined domain-specific knowledge and reading/writing group-level factor, with model size positively correlating with these factors but showing diminishing returns.
🎬 And that's a wrap! Stay tuned for the latest AI news and trends 🌟
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will jump into the last one for sure - everything that scares us is always the most interesting! :-)