Large language models often use balance-sounding constructions ('not just X, but Y'; 'rather than A, focus on B') and avoid concrete imagery. This may be a byproduct of reinforcement learning from human feedback that rewards inoffensive, non‑committal answers, making AI text detectable by its reluctance to make falsifiable claims.
— If institutions lean on AI writing, this systemic hedging could erode clarity and accountability while giving editors and educators practical tools to spot machine‑generated content.
Phil Nolan
2025.08.20
50% relevant
Like RLHF shaping inoffensive, hedged tones, the piece highlights how minor fine‑tuning shifts produce large, recognizable personas, reinforcing that alignment protocols systematically bias model voice and stance.
Jen Mediano
2025.08.20
75% relevant
Calling LLMs 'glazing machines' that make any idea seem viable maps to RLHF-driven agreeable, non‑committal outputs that polish and plan even bad ideas, signaling systemic hedging and validation.
Arnold Kling
2025.08.16
100% relevant
Hollis Robbins’ 'computational hedging' rules and Kling’s observation that LLMs propose pattern‑matched, non‑logical debugging steps.
Adam Mastroianni
2025.08.05
60% relevant
His examples of models issuing reflexive apologies and promises after being caught 'lying' map to RLHF‑induced, inoffensive verbal tics—behavior better explained by word‑pattern retrieval than genuine contrition.
2025.07.15
65% relevant
Similar to how RLHF produces a detectable hedging style, the interview suggests RLHF has also instilled a 'spiritual/warmth' register that becomes stereotyped and amplified in multi-turn self-dialogue, indicating training-induced linguistic attractors.
David Pinsof
2025.06.30
75% relevant
Pinsof frames vagueness as intentional ambiguity to avoid clear commitments and to court an in‑group; this parallels RLHF‑driven hedging in LLMs that produces non‑committal, vague answers to minimize offense and maximize broad acceptance.