@inproceedings{cochintele-etal-2026-one,
title = "One Workflow Does Not Fit All Relations: A System Description for the {AKBC} 2026 {LM}-{KBC} Shared Task",
author = "Cochintele, Andreea-Elena and
Dobrov{\u{a}}ț, Anca and
Nisioi, Sergiu",
editor = "Kalo, Jan-Christoph and
Nakashole, Ndapa and
Razniewski, Simon and
Suchanek, Fabian M. and
Vlachos, Andreas and
McCallum, Andrew",
booktitle = "Proceedings of the 11th Workshop on Automated Knowledge Base Construction ({AKBC} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.akbc-1.41/",
pages = "527--534",
ISBN = "979-8-89176-464-4",
abstract = "This paper presents a system developed for the fifth AKBC Shared Task on Knowledge Base Construction from Language Models (LM-KBC 2026). The task involves predicting sets of object-entity for (subject, relation) pairs across six relation types, ranging from single-valued relations to relations with potentially hundreds of recipients. All predictions are generated in a closed-book setting (CITATION), utilizing up to 32 billion parameters across all neural components, without resorting to fine-tuning or retrieval augmented techniques. The development process included two stages: validating the technical methodology using the officially specified baseline model (\textit{Qwen3.5-9B}), followed by comparing nine open-weight models using the same evaluation framework. This process led to the selection of Gemma 3 27B as the final system. The paper presents the main output-parsing errors identified and resolved, together with an evaluation of self-consistency and majority-voting aggregation for each relation type. The final system improved macro-F1 score from 0.313 to 0.531 (a +70{\%} relative improvement) through workflow optimization and model selection."
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%0 Conference Proceedings
%T One Workflow Does Not Fit All Relations: A System Description for the AKBC 2026 LM-KBC Shared Task
%A Cochintele, Andreea-Elena
%A Dobrovăț, Anca
%A Nisioi, Sergiu
%Y Kalo, Jan-Christoph
%Y Nakashole, Ndapa
%Y Razniewski, Simon
%Y Suchanek, Fabian M.
%Y Vlachos, Andreas
%Y McCallum, Andrew
%S Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-464-4
%F cochintele-etal-2026-one
%X This paper presents a system developed for the fifth AKBC Shared Task on Knowledge Base Construction from Language Models (LM-KBC 2026). The task involves predicting sets of object-entity for (subject, relation) pairs across six relation types, ranging from single-valued relations to relations with potentially hundreds of recipients. All predictions are generated in a closed-book setting (CITATION), utilizing up to 32 billion parameters across all neural components, without resorting to fine-tuning or retrieval augmented techniques. The development process included two stages: validating the technical methodology using the officially specified baseline model (Qwen3.5-9B), followed by comparing nine open-weight models using the same evaluation framework. This process led to the selection of Gemma 3 27B as the final system. The paper presents the main output-parsing errors identified and resolved, together with an evaluation of self-consistency and majority-voting aggregation for each relation type. The final system improved macro-F1 score from 0.313 to 0.531 (a +70% relative improvement) through workflow optimization and model selection.
%U https://aclanthology.org/2026.akbc-1.41/
%P 527-534
Markdown (Informal)
[One Workflow Does Not Fit All Relations: A System Description for the AKBC 2026 LM-KBC Shared Task](https://aclanthology.org/2026.akbc-1.41/) (Cochintele et al., AKBC 2026)
ACL