HUMHUM-003 — Human-AI Collaboration Design Flaws
LLM Decoding Randomness Causes Compounding Hallucination Errors
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Autoregressive token generation in large language models accumulates errors, while standard sampling strategies introduce randomness that systematically increases hallucination rates. Organisations deploying LLMs in consequential workflows face material risk of confident, plausible, and incorrect outputs that evade routine quality controls.
Domain
Human Factors
Blindspots in change management, skills, human-AI collaboration, trust, workforce, and culture.
Source
MIT AI Risk Repository — Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024) ↗https://airisk.mit.edu/
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