GenGO Ultra: an LLM-powered ACL Paper Explorer

The ever-growing number of papers in natural language processing (NLP) poses the challenge of finding relevant papers.In our previous paper, we introduced GenGO (Takeshita et al., 2024b), which complements NLP papers with various information, such as aspect-based summaries, to enable efficient paper exploration.While it delivers a better literature search experience, it lacks an interactive interface that dynamically produces information tailored to the user's needs.To this end, we present an extension to our previous system, dubbed GenGO Ultra, which exploits large language models (LLMs) to dynamically generate responses grounded by published papers.We also conduct multi-granularity experiments to evaluate six text encoders and five LLMs.Our system is designed for transparency -based only on open-weight models, visible system prompts, and an open-source code base -to foster further development and research on top of our system: