@inproceedings{takeshita-etal-2025-gengo,
title = "{G}en{GO} Ultra: an {LLM}-powered {ACL} Paper Explorer",
author = "Takeshita, Sotaro and
Tsereteli, Tornike and
Ponzetto, Simone Paolo",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.24/",
doi = "10.18653/v1/2025.acl-demo.24",
pages = "242--251",
ISBN = "979-8-89176-253-4",
abstract = "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, 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: https://gengo-ultra.sotaro.io/"
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%0 Conference Proceedings
%T GenGO Ultra: an LLM-powered ACL Paper Explorer
%A Takeshita, Sotaro
%A Tsereteli, Tornike
%A Ponzetto, Simone Paolo
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F takeshita-etal-2025-gengo
%X 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, 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: https://gengo-ultra.sotaro.io/
%R 10.18653/v1/2025.acl-demo.24
%U https://aclanthology.org/2025.acl-demo.24/
%U https://doi.org/10.18653/v1/2025.acl-demo.24
%P 242-251
Markdown (Informal)
[GenGO Ultra: an LLM-powered ACL Paper Explorer](https://aclanthology.org/2025.acl-demo.24/) (Takeshita et al., ACL 2025)
ACL
- Sotaro Takeshita, Tornike Tsereteli, and Simone Paolo Ponzetto. 2025. GenGO Ultra: an LLM-powered ACL Paper Explorer. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 242–251, Vienna, Austria. Association for Computational Linguistics.