Cross-Domain Generalization for Training LLM Monitors
A recent preprint on arXiv (2605.12265) explores the impact of training language models across various classification tasks on their effectiveness in unfamiliar domains. The research indicates that this approach somewhat generalizes to related domains, improving classification accuracy on novel tasks. Nonetheless, there are specific edge cases where fine-tuned models struggle to adhere to prompts, particularly when the prompt for classification shifts drastically, despite the data domain staying constant. Combining classification training with general instruction adherence can help reduce these issues while preserving the advantages.
Key facts
- arXiv preprint 2605.12265
- Studies cross-domain generalization for LLM monitors
- Training on multiple classification tasks improves performance on new domains
- Edge cases: models fail when prompt changes but domain stays same
- Mixing classification and instruction following training mitigates failures
- Prompted language models used as classifiers
- Fine-tuning offers robustness and performance benefits
Entities
Institutions
- arXiv