Context:Despite the impressive capabilities of Large Language Models (LLMs), they pose significant risks in many domains and therefore require guardrails throughout the lifecycle.Problem:Many such guardrails are trained as classifiers with domain-specific human text datasets obtained from sources such as social media and they achieve reasonable performance against closed-domain benchmarks. When deployed in the real world, however, the guardrails have to deal with machine text in an open domain, and their performance deteriorates drastically, rendering them almost unusable due to a high level of false refusal.Solution:In this paper, using a self-harm detector as an example, we demonstrate the specific challenges facing guardrail deployment due to the data drift between training and production environments. More specifically, we formed two hypotheses about the potential causes, i.e. closed vs. open domain, human vs. LLM-generated text, and conducted five experiments to explore various potential remedies, including their respective advantages and disadvantages.Evaluation:While focusing on one example, our experience and knowledge of LLM guardrails give us great confidence that our work contributes to a more thorough understanding of guardrail deployment and can be generalized as a methodology to build more robust domain-specific guardrails in real-world applications.

The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise applications. Therefore, LLMs need to be benchmarked with enterprise datasets for a variety of NLP tasks. This work explores benchmarking strategies focused on LLM evaluation, with a specific emphasis on both English and Japanese. The proposed evaluation framework encompasses 25 publicly available domain-specific English benchmarks from diverse enterprise domains like financial services, legal, climate, cyber security, and 2 public Japanese finance benchmarks. The diverse performance of 8 models across different enterprise tasks highlights the importance of selecting the right model based on the specific requirements of each task. Code and prompts are available on GitHub.