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As the use of large language models (LLMs) increases within society, as doesthe risk of their misuse. Appropriate safeguards must be in place to ensure LLMoutputs uphold the ethical standards of society, highlighting the positive rolethat artificial intelligence technologies can have. Recent events indicateethical concerns around conventionally trained LLMs, leading to overall unsafeuser experiences. This motivates our research question: how do we ensure LLMalignment? In this work, we introduce a test suite of unique prompts to fosterthe development of aligned LLMs that are fair, safe, and robust. We show thatprompting LLMs at every step of the development pipeline, including datacuration, pre-training, and fine-tuning, will result in an overall moreresponsible model. Our test suite evaluates outputs from four state-of-the-artlanguage models: GPT-3.5, GPT-4, OPT, and LLaMA-2. The assessment presented inthis paper highlights a gap between societal alignment and the capabilities ofcurrent LLMs. Additionally, implementing a test suite such as ours lowers theenvironmental overhead of making models safe and fair.

She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models