New method estimates tail risks in language model outputs
A novel technique has been introduced to assess the likelihood of infrequent harmful outputs generated by language models. Given that these models are utilized billions of times each day, even the most improbable negative behaviors can manifest. Existing safety assessments primarily concentrate on input distributions, overlooking the probabilistic characteristics of the models and their tail output behaviors. The new method employs importance sampling, generating unsafe variations of the target model to effectively estimate the probabilities of harmful outputs, avoiding the need for exhaustive sampling.
Key facts
- arXiv:2604.22167
- Language models are deployed at population-level scale
- Harmful outputs are rare but occur due to high query volume
- Current safety evaluations disregard tail output behavior
- Proposed method uses importance sampling
- Unsafe versions of the target model are created for estimation
- Method enables sample-efficient estimation
- Focus on tail risk in language model outputs
Entities
Institutions
- arXiv