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Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering unwarranted flattery, and giving advice skewed toward users'expressed views. In reality, sycophancy rarely happens in a single exchange; it m...
This account unifies alignment faking, sandbagging, and evaluation-aware scheming and reorients the remedy: not deeper internalization but architecture, making violations unavailable rather than unchosen.
The Last Translation Benchmark is introduced, a collection of human-authored and peer-reviewed examples that break leading machine translation models and a new evaluation approach: each example comes with handcrafted verification rules describing concrete failure cases on that example, therefore allowing reliable and a...
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