arXiv cs.CLPaper
Correctness Is a Direction: Geometric Answer Selection in Language Models
This is immediately useful. The method is simple (one forward pass, one dot product), requires no generation, and works across model families. For builders using small LLMs (1-8B), this is a better hallucination detector than existing approaches. You can train it on fifty examples and get strong AUROC on factual tasks. For inference, the overhead is negligible. Test this on your domain and consider swapping it in for probability-based baselines. The directions are interpretable too—correctness is a learnable signal.