Domain-Conditional Position Offsets Improve Language Model Cold-Start Accuracy
A new method called domain-conditional position offsets, which involves adding a single learned vector to the first token embeddings, has been shown to reduce the cold-start penalty in autoregressive language models. This approach trains in minutes on about 100 documents, requires no changes to model weights, and reduces in-domain perplexity by up to 27% across models ranging from 410M to 8B parameters. It outperforms direct logit-bias correction and is more lightweight than LoRA or soft prompts.
Why it matters: This technique provides a practical, low-cost way to improve language model accuracy on short, domain-specific inputs without retraining or added latency.
Full story at: arXiv Machine Learning ↗