Design-Inclusive Language Models for Responsible Information Access

As the use of large language models (LLMs) increases for everyday tasks, appropriate safeguards must be in place to ensure unbiased and safe output. Recent events highlight ethical concerns around conventionally trained LLMs, leading to overall unsafe user experiences. This motivates the need for responsible LLMs that are trained fairly, transparent to the public, and regularly monitored after deployment. In this work, we introduce the "Responsible Development of Language Models (ReDev)" framework to foster the development of fair, safe, and robust LLMs for all users. We also present a test suite of unique prompt types to assess LLMs on the aforementioned elements, ensuring all generated responses are non-harmful and free from biased content. Outputs from four state-of-the-art LLMs, OPT, GPT-3.5, GPT-4, and LLaMA-2, are evaluated by our test suite, highlighting the importance of considering fairness, safety, and robustness at every stage of the machine learning pipeline, including data curation, training, and post-deployment.