Hallucination benchmark release coincided with rapid model improvement — what that means for alignment
StrategicHarmony · reddit · 2026-09-10
The author notes that after a public hallucination-rate benchmark (arXiv:2511.13029) was published late last year, nearly every model scoring above zero was released afterward, with frontier models steadily improving — suggesting measurement itself reshaped incentives.
Key arguments:
- Incentives: benchmarks counting only correct answers rewarded guessing; once hallucinations are measured and punished, refusal becomes trainable and hallucination rates drop dramatically.
- On benchmaxxing: models will always be trained to benchmarks, but broader and more accurate measurement narrows the gap between test-taking and real capability.
Alignment implications:
- Users need benchmarks for transparency, honesty, and obedience: does the model hide what it does, do tool calls and CoT match reports, does it obey instructions?
- For side-effects and weaponisation, the author argues it's a competition/cost/openness problem: broad access for defenders is safer than a few closed providers.
More from AGI Musings
- AI safety circles debate p(doom): Hubinger's >10%, FTC Chair's 15% — Miles_Brundage · 2026-09-10
- "Too Many People, Not Enough Work" — While the Company Runs Four AI Projects — 914paul · 2026-09-10
- Why Anthropic's ECON Report Assumes Robotics Won't Automate 'Non-Knowledge' Jobs — macnfly23 · 2026-09-10
- xAI co-founder slams AI doomers, citing their call for a WWII-style anti-AI crusade — beffjezos · 2026-09-10
- AI safety debate: skeptics demand a doom scenario that doesn't read like sci-fi — Miles_Brundage · 2026-09-10
- Why one relatable guy beat the rationalists' billions at making the case to pause AI — gabriel1 · 2026-09-10