  The ever-growing corpus of scientific literature presents significant
challenges for researchers with respect to discovery, management, and
annotation of relevant publications. Traditional platforms like Semantic
Scholar, BibSonomy, and Zotero offer tools for literature management, but
largely require manual laborious and error-prone input of tags and metadata.
Here, we introduce a novel retrieval augmented generation system that leverages
chat-based large language models (LLMs) to streamline and enhance the process
of publication management. It provides a unified chat-based interface, enabling
intuitive interactions with various backends, including Semantic Scholar,
BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1)
Explorative Search & Retrieval - leveraging LLMs to search for and retrieve
both specific and general scientific publications, while addressing the
challenges of content hallucination and data obsolescence; and (2) Cataloguing
& Management - aiding in the organization of personal publication libraries, in
this case BibSonomy, by automating the addition of metadata and tags, while
facilitating manual edits and updates. We compare our system to different LLM
models in three different settings, including a user study, and we can show its
advantages in different metrics.
