Large language model applications for evaluation: Opportunities and et hical implications - Head, Cari Beth; Jasper, Paul; McConnachie, Matthew; Raftree, Linda; Higdon, Grace (New Directions for Evaluation, 2023)
Large language models (LLMs) are a type of generative artificial intel ligence (AI) designed to produce text-based content. LLMs use deep lea rning techniques and massively large data sets to understand, summariz e, generate, and predict new text. LLMs caught the public eye in early 2023 when ChatGPT (the first consumer facing LLM) was released. LLM t echnologies are driven by recent advances in deep-learning AI techniqu es, where language models are trained on extremely large text data fro m the internet and then re-used for downstream tasks with limited fine -tuning required. They offer exciting opportunities for evaluators to automate and accelerate time-consuming tasks involving text analytics and text generation. We estimate that over two-thirds of evaluation ta sks will be affected by LLMs in the next 5 years. Use-case examples in clude summarizing text data, extracting key information from text, ana lyzing and classifying text content, writing text, and translation. De spite the advances, the technologies pose significant challenges and r isks. Because LLM technologies are generally trained on text from the internet, they tend to perpetuate biases (racism, sexism, ethnocentris m, and more) and exclusion of non-majority languages. Current tools li ke ChatGPT have not been specifically developed for monitoring, evalua tion, research, and learning (MERL) purposes, possibly limiting their accuracy and usefulness for evaluation. In addition, technical limitat ions and challenges with bias can lead to real world harm. To overcome these technical challenges and ethical risks, the evaluation communit y will need to work collaboratively with the data science community to co-develop tools and processes and to ensure the application of quali ty and ethical standards.