Undergraduate Assistant to create AI-Enhanced Library with Finetuning Large Language Models

Part time Undergraduate Assistant to create AI-Enhanced Library with Finetuning Large Language Models

We are seeking a motivated undergraduate student to assist with an innovative research project aimed at fine-tuning large language models (LLMs) on domain-specific digital collections. The project explores how customized AI models can enhance access, interpretation, and engagement with library archives, manuscripts, and other digital materials.

This is an exciting opportunity for a student interested in the intersection of technology and the humanities to gain hands-on experience in applied AI, digital scholarship, and natural language processing.

Responsibilities

• Assist in preparing and labeling text-based datasets from digital collections (e.g., metadata, archival documents).
• Support fine-tuning and evaluation of large language models (e.g., GPT, LLaMA) using Python-based machine learning frameworks.
• Collaborate in testing model outputs for relevance, accuracy, and ethical alignment in a humanities research context.
• Document workflows and contribute to a final report or presentation on findings.
• Attend regular project meetings and maintain effective communication with the research team.

Required Qualifications:

• Current undergraduate student in Computer Science, Information Systems, or a related field.
• Basic programming knowledge (e.g., Python, JavaScript) and familiarity with large language models (e.g. GPT, Llama).
• Strong communication and organizational skills.
• Interest in AI applications on digital collections, archives, or library science.

Preferred Qualifications

• Undergraduate student majoring in Humanities or Social Sciences (e.g., History, English, Anthropology, Sociology, Philosophy) with a minor or coursework in Computer Science, Data Science, or Digital Humanities.
• Basic understanding of or strong interest in natural language processing and/or machine learning.
• Strong writing, research, and analytical skills.
• Problem-solving mindset and ability to work independently and collaboratively.

Benefits

• Gain hands-on experience in machine learning and LLM applications.
• Work closely with faculty, librarians, and technologists.
• Contribute to cutting-edge digital scholarship with real-world impact.
• Potential for authorship on presentations or publications.

How to Apply:

Submit your resume, a brief statement of interest (max 250 words), and any relevant project/work samples to vandana@sc.edu or boydkf@mailbox.sc.edu  by June 30, 2025.