3 papers
cs.NE2026
Analysis and Explainability of LLMs Via Evolutionary Methods
Shannon K. Gallagher, Swati Rallapalli, Tyler Brooks +3
Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend these methods to neural networks,…
cs.CL2026
Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies
Swati Rallapalli, Shannon Gallagher, Ronald Yurko +4
Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such as large scale spam, phishing…
cs.CL2026
Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
Swati Rallapalli, Shannon Gallagher, Andrew O. Mellinger +6
We study the efficacy of fine-tuning Large Language Models (LLMs) for the specific task of report (government archives, news, intelligence reports) summarization. While this topic…