Turns out LLMs often know they’re hallucinating – this startup uses that insight to slash errors GPT-4 to GPT-5 style.
You’ve fine-tuned for hours, added RAG, grounded outputs… and hallucinations still sneak through. What if models secretly knew they were lying all along?
Goodire, fresh off a $150M Series B at $1.25B valuation, cracked AI interpretability to reveal LLMs frequently detect their own hallucinations internally.[1] Using mechanistic interpretability, they isolate these self-awareness signals with high accuracy, then feed them back as training data. Result? Hallucination reductions rivaling hypothetical GPT-4 to GPT-5 leaps—in months, not years.
Direct dev impact: This isn’t academic. Goodire’s approach delivers immediate wins: fewer guardrails needed, higher trust in ungrounded responses, reliable open-source deployments. Imagine shipping customer-facing chatbots that self-censor confidently wrong answers. It’s knowledge transfer from AI to human—models teaching us their blind spots.
Vs. current solutions (self-consistency, verifiers), this goes deeper: internal model states, not just output inspection. They’ve also applied it to biomedicine, finding novel Alzheimer’s biomarkers. Competitive landscape? Anthropic’s work meets startup speed—watch if xAI or DeepMind acquire similar tech.
Try it now: Goodire’s pilot results suggest this scales. Fork their methods if open-sourced, or pitch your hallucination-plagued project. Early access could 10x your model’s reliability. Bigger question: if models know their limits, what else are they hiding?
Source: Storytelling Edge