Beyond Neutrality: Comparative Study of ChatGPT-Produced Recommendation Letters

Abstract

This study explores LLM-generated documents, their social impacts and biases, and their outcomes in research. Language, as a deeply intricate and nuanced system, continues to challenge our efforts in both understanding and generation. Language modeling has progressed from foundational statistical techniques to the sophistication of Large Language Models (LLMs) powered by deep learning. Trained on vast quantities of data, self-generated content, and self- and semi supervised learning, demonstrate remarkable capabilities for producing contextually relevant, human-like text and executing a broad spectrum of language tasks. Despite their promise, these systems are not without limitations, particularly concerning embedded biases stemming from training data and model architecture. Emerging scholarship delves into their symbolic reasoning, potential, and interdisciplinary applications, though many avenues remain insufficiently explored. As examples of generative artificial intelligence, ChatGPT increasingly contributes to education by fostering accessible learning, facilitating domain-specific assessments, and nurturing creativity and critical thinking among learners. It attempts to understand the LLM model by unraveling the facts in day-to-day practices. This article applies the qualitative methods used in comparing two AI-generated recommendation letters, indicating male and female (Ajit/ Ajita). Textual review as a major tool and technique of knowing LLMs and their biases, significance, social impacts, and the ways of interpreting biases with outcomes. It is based on sociotechnical system theory and algorism bias theory. This article claims that LLMs are the latest AI-generated product utilized in academic discourses, enriching the capacity of the user. The biases could be minimized by careful attention, capacity, tactful utilization, and knowledge of the user.