One thing I’ve been thinking about is whether current alignment strategies in LLMs are starting to prioritize safety signals (e.g. avoidance, hedging, refusal) over epistemic usefulness, especially in ambiguous or edge-case queries. In theory, a well-aligned system should still be able to provide useful, bounded, or uncertainty-aware responses instead of defaulting to avoidance. But in practice, many systems seem to fall back to conservative patterns even when a nuanced answer might be possible. Is this mainly a limitation of current alignment techniques like RLHF and policy shaping, or is it an intentional design choice to minimize tail-risk at scale? I’m also curious whether there are active approaches (e.g. constitutional AI, calibrated uncertainty, or better intent modeling) that meaningfully reduce over-refusal without increasing risk. submitted by /u/NoFilterGPT
Originally posted by u/NoFilterGPT on r/ArtificialInteligence