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14 papers from Google DeepMind on Natural Language Processing
AI ideas can be reliably detected even when the prose is human-written, outperforming traditional detectors that focus on writing style.
Long-term interactions with language models could lead to significant cognitive and emotional changes in users that short-term evaluations miss entirely.
TokenMinds reveals that combining discrete SID-based user tokens with dense embeddings can significantly enhance user modeling in recommender systems at scale.
Language adherence in ASR can be significantly improved using soft prompting techniques, leading to better transcription quality in multilingual contexts.
Calibrated safety flags in medical summaries can reduce unflagged omissions by up to 5 times compared to existing methods, enhancing clinician confidence in LLM outputs.
Training a foundation model on a trillion minutes of wearable sensor data unlocks surprisingly accurate predictions across a wide range of health conditions, even with limited labeled data.
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.