Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation
arXiv:2606.25782v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effective while remaining low-cost and low-latency. Safety evaluation of LLM outputs has generally relied on LLM-based judges, which can be effective but are often slow and expensive to deploy at scale. In this paper, we evaluate whether fine-tuned modern encoder classifiers from the ModernBERT family, including ModernBERT and Ettin, can reliably identify harmful LLM outputs in user-model conversation
The rapid deployment of LLMs necessitates more efficient and scalable safety guardrails to mitigate risks associated with their widespread adoption.
This research provides a pathway to lower the cost and latency of LLM safety evaluations, enabling faster iteration and safer integration into products.
The ability to use more cost-effective encoder models for safety judgements could democratize advanced LLM safety evaluation, accelerating deployment cycles for various applications.
- · AI developers
- · LLM guardrail providers
- · Companies deploying LLMs
- · Expensive LLM-based safety judges
More efficient and cost-effective LLM safety evaluations become widely accessible.
Faster and safer deployment of new LLM applications across various industries.
Enhanced trust in LLM applications due to more rigorous and scalable safety protocols, potentially accelerating AI adoption.
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