  In an era marked by a rapid increase in scientific publications, researchers
grapple with the challenge of keeping pace with field-specific advances. We
present the `AHAM' methodology and a metric that guides the domain-specific
\textbf{adapt}ation of the BERTopic topic modeling framework to improve
scientific text analysis. By utilizing the LLaMa2 generative language model, we
generate topic definitions via one-shot learning by crafting prompts with the
\textbf{help} of domain experts to guide the LLM for literature mining by
\textbf{asking} it to model the topic names. For inter-topic similarity
evaluation, we leverage metrics from language generation and translation
processes to assess lexical and semantic similarity of the generated topics.
Our system aims to reduce both the ratio of outlier topics to the total number
of topics and the similarity between topic definitions. The methodology has
been assessed on a newly gathered corpus of scientific papers on
literature-based discovery. Through rigorous evaluation by domain experts, AHAM
has been validated as effective in uncovering intriguing and novel insights
within broad research areas. We explore the impact of domain adaptation of
sentence-transformers for the task of topic \textbf{model}ing using two
datasets, each specialized to specific scientific domains within arXiv and
medarxiv. We evaluate the impact of data size, the niche of adaptation, and the
importance of domain adaptation. Our results suggest a strong interaction
between domain adaptation and topic modeling precision in terms of outliers and
topic definitions.
