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8 papers from Google DeepMind on Natural Language Processing
Ethics interventions in AI development often fail because practitioners don't trust them – here's a breakdown of why, and how to fix it.
Unpacking Google's AI literacy partnerships reveals the surprising complexities of aligning research, industry, and public needs.
LLMs' struggle to grasp subtext—even generating literal clues 60% of the time—reveals a critical gap in their ability to understand nuanced human communication.
LLM-powered diagnostic AI is ready for prime time: a real-world clinical trial shows it's safe, patients love it, and doctors find it useful.
Achieve significantly better code generation and mathematical problem solving from diffusion language models with a simple, training-free sampling tweak that encourages diversity.
LLMs can be taught to proactively seek and effectively use conversational feedback, generalizing across tasks and improving their ability to handle ambiguity.
Forget scaling laws: teaching LLMs to learn from feedback lets smaller models rival giants and generalize to new tasks.
People prefer AI advisors, but AI delegates that autonomously negotiate on their behalf actually lead to higher individual gains and improve overall group welfare in multi-party bargaining games.
