A Critical Review of Large Language Models: Sensitivity, Bias, and the
  Path Toward Specialized AI
Arash Hajikhani Carolyn Cole
This paper examines the comparative effectiveness of a specialized compiled
language model and a general-purpose model like OpenAI's GPT-3.5 in detecting
SDGs within text data. It presents a critical review of Large Language Models
(LLMs), addressing challenges related to bias and sensitivity. The necessity of
specialized training for precise, unbiased analysis is underlined. A case study
using a company descriptions dataset offers insight into the differences
between the GPT-3.5 and the specialized SDG detection model. While GPT-3.5
boasts broader coverage, it may identify SDGs with limited relevance to the
companies' activities. In contrast, the specialized model zeroes in on highly
pertinent SDGs. The importance of thoughtful model selection is emphasized,
taking into account task requirements, cost, complexity, and transparency.
Despite the versatility of LLMs, the use of specialized models is suggested for
tasks demanding precision and accuracy. The study concludes by encouraging
further research to find a balance between the capabilities of LLMs and the
need for domain-specific expertise and interpretability.