@inproceedings{kalo-etal-2026-summary,
title = "Summary of the {AKBC} 2026 Shared Task: Language Models for Knowledge Base Construction ({LM}-{KBC}, 5th Edition)",
author = "Kalo, Jan-Christoph and
Razniewski, Simon and
Zhang, Bohui and
Nguyen, Tuan-Phong",
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-1.28/",
pages = "409--416",
ISBN = "979-8-89176-464-4",
abstract = "Pretrained language models (LMs) have significantly advanced a variety of knowledge engineering tasks and have shown promise as sources of factual knowledge. While prior work has studied factual knowledge through probing and prompting, reliably constructing complete knowledge bases from the parametric knowledge of LMs remains challenging. The AKBC 2026 Shared Task invited participants to construct knowledge bases directly from LMs, given specific subjects and relations. Unlike existing probing benchmarks, the challenge imposed no simplifying assumptions on relation cardinality, allowing a subject entity to be associated with zero, one, or multiple objects, including long answer sets. To ensure accessibility, the challenge featured a single track using open-weight LMs with a total neural parameter budget of up to 32B. Nineteen submissions were received, of which twelve were ranked, exploring a variety of approaches including relation-specific prompting, evidence aggregation, and model selection."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kalo-etal-2026-summary">
<titleInfo>
<title>Summary of the AKBC 2026 Shared Task: Language Models for Knowledge Base Construction (LM-KBC, 5th Edition)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jan-Christoph</namePart>
<namePart type="family">Kalo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Razniewski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bohui</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tuan-Phong</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jan-Christoph</namePart>
<namePart type="family">Kalo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ndapa</namePart>
<namePart type="family">Nakashole</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Razniewski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fabian</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Suchanek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">Vlachos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrew</namePart>
<namePart type="family">McCallum</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-464-4</identifier>
</relatedItem>
<abstract>Pretrained language models (LMs) have significantly advanced a variety of knowledge engineering tasks and have shown promise as sources of factual knowledge. While prior work has studied factual knowledge through probing and prompting, reliably constructing complete knowledge bases from the parametric knowledge of LMs remains challenging. The AKBC 2026 Shared Task invited participants to construct knowledge bases directly from LMs, given specific subjects and relations. Unlike existing probing benchmarks, the challenge imposed no simplifying assumptions on relation cardinality, allowing a subject entity to be associated with zero, one, or multiple objects, including long answer sets. To ensure accessibility, the challenge featured a single track using open-weight LMs with a total neural parameter budget of up to 32B. Nineteen submissions were received, of which twelve were ranked, exploring a variety of approaches including relation-specific prompting, evidence aggregation, and model selection.</abstract>
<identifier type="citekey">kalo-etal-2026-summary</identifier>
<location>
<url>https://aclanthology.org/2026.akbc-1.28/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>409</start>
<end>416</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Summary of the AKBC 2026 Shared Task: Language Models for Knowledge Base Construction (LM-KBC, 5th Edition)
%A Kalo, Jan-Christoph
%A Razniewski, Simon
%A Zhang, Bohui
%A Nguyen, Tuan-Phong
%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 kalo-etal-2026-summary
%X Pretrained language models (LMs) have significantly advanced a variety of knowledge engineering tasks and have shown promise as sources of factual knowledge. While prior work has studied factual knowledge through probing and prompting, reliably constructing complete knowledge bases from the parametric knowledge of LMs remains challenging. The AKBC 2026 Shared Task invited participants to construct knowledge bases directly from LMs, given specific subjects and relations. Unlike existing probing benchmarks, the challenge imposed no simplifying assumptions on relation cardinality, allowing a subject entity to be associated with zero, one, or multiple objects, including long answer sets. To ensure accessibility, the challenge featured a single track using open-weight LMs with a total neural parameter budget of up to 32B. Nineteen submissions were received, of which twelve were ranked, exploring a variety of approaches including relation-specific prompting, evidence aggregation, and model selection.
%U https://aclanthology.org/2026.akbc-1.28/
%P 409-416
Markdown (Informal)
[Summary of the AKBC 2026 Shared Task: Language Models for Knowledge Base Construction (LM-KBC, 5th Edition)](https://aclanthology.org/2026.akbc-1.28/) (Kalo et al., AKBC 2026)
ACL