@inproceedings{hamilton-etal-2026-corporatebench,
title = "{C}orporate{B}ench: Large-Scale {Q}{\&}{A} Benchmarking with Temporal Knowledge Bases",
author = "Hamilton, Sil and
Sun, Albert and
Romero, Oscar J. and
Henneking, Carl-Leander and
Mimno, David and
Yang, Bishan and
Labutov, Igor",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.34/",
pages = "667--701",
ISBN = "979-8-89176-462-0",
abstract = "LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don{'}t want to share internal communications, and synthetic datasets have been overly simple. We present CorporateBench (CB), a human-validated multi-task Q A benchmark whose scale approaches the conditions LLMs encounter in corporate communication networks, with evaluation corpora surpassing 230,000 documents. CB evaluates LLMs across two dimensions (information extraction and knowledge base querying) through four synthetically generated firms ranging from 12 to 10,000 employees. Each corpus is sampled from a temporally evolving knowledge base describing a consistent world, guaranteeing cross-document logical consistency even across hundreds of thousands of documents. We evaluate five LLMs on CB, revealing increasingly poor performance as input size approaches realistic scales. CB provides LLM developers a metric for corporate communication reasoning, filling a crucial gap in the benchmarking ecosystem."
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<abstract>LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don’t want to share internal communications, and synthetic datasets have been overly simple. We present CorporateBench (CB), a human-validated multi-task Q A benchmark whose scale approaches the conditions LLMs encounter in corporate communication networks, with evaluation corpora surpassing 230,000 documents. CB evaluates LLMs across two dimensions (information extraction and knowledge base querying) through four synthetically generated firms ranging from 12 to 10,000 employees. Each corpus is sampled from a temporally evolving knowledge base describing a consistent world, guaranteeing cross-document logical consistency even across hundreds of thousands of documents. We evaluate five LLMs on CB, revealing increasingly poor performance as input size approaches realistic scales. CB provides LLM developers a metric for corporate communication reasoning, filling a crucial gap in the benchmarking ecosystem.</abstract>
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%0 Conference Proceedings
%T CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
%A Hamilton, Sil
%A Sun, Albert
%A Romero, Oscar J.
%A Henneking, Carl-Leander
%A Mimno, David
%A Yang, Bishan
%A Labutov, Igor
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hamilton-etal-2026-corporatebench
%X LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don’t want to share internal communications, and synthetic datasets have been overly simple. We present CorporateBench (CB), a human-validated multi-task Q A benchmark whose scale approaches the conditions LLMs encounter in corporate communication networks, with evaluation corpora surpassing 230,000 documents. CB evaluates LLMs across two dimensions (information extraction and knowledge base querying) through four synthetically generated firms ranging from 12 to 10,000 employees. Each corpus is sampled from a temporally evolving knowledge base describing a consistent world, guaranteeing cross-document logical consistency even across hundreds of thousands of documents. We evaluate five LLMs on CB, revealing increasingly poor performance as input size approaches realistic scales. CB provides LLM developers a metric for corporate communication reasoning, filling a crucial gap in the benchmarking ecosystem.
%U https://aclanthology.org/2026.findings-emnlp.34/
%P 667-701
Markdown (Informal)
[CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases](https://aclanthology.org/2026.findings-emnlp.34/) (Hamilton et al., Findings 2026)
ACL