@inproceedings{asano-etal-2026-mobqa,
title = "{M}ob{QA}: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering",
author = "Asano, Hikaru and
Ouchi, Hiroki and
Kasuga, Akira and
Yonetani, Ryo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2223/",
pages = "50602--50633",
ISBN = "979-8-89176-459-0",
abstract = "This paper presents MobQA, a benchmark dataset designed to evaluate the semantic understanding capabilities of large language models (LLMs) for human mobility data through natural language question answering. While existing models predict human movement patterns well, their ability to interpret underlying reasons or semantic meaning remains unclear. To address this gap, we annotated 14,260 trajectory points from the GeoLife dataset with 30,703 semantic labels covering points of interest, mobility modes, and visit reasons, grounding semantics-dependent questions and reference answers. Building on these annotations, MobQA provides 5,800 question{--}answer pairs from daily and weekly GPS trajectories, spanning factual retrieval, multiple-choice semantic inference, and free-form interpretive explanation. Our evaluation of major LLMs reveals strong performance on factual retrieval but notable limits in semantic reasoning and explanation, with trajectory length substantially impacting effectiveness. These findings demonstrate both achievements and limitations of recent LLMs for semantic mobility understanding."
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<abstract>This paper presents MobQA, a benchmark dataset designed to evaluate the semantic understanding capabilities of large language models (LLMs) for human mobility data through natural language question answering. While existing models predict human movement patterns well, their ability to interpret underlying reasons or semantic meaning remains unclear. To address this gap, we annotated 14,260 trajectory points from the GeoLife dataset with 30,703 semantic labels covering points of interest, mobility modes, and visit reasons, grounding semantics-dependent questions and reference answers. Building on these annotations, MobQA provides 5,800 question–answer pairs from daily and weekly GPS trajectories, spanning factual retrieval, multiple-choice semantic inference, and free-form interpretive explanation. Our evaluation of major LLMs reveals strong performance on factual retrieval but notable limits in semantic reasoning and explanation, with trajectory length substantially impacting effectiveness. These findings demonstrate both achievements and limitations of recent LLMs for semantic mobility understanding.</abstract>
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%0 Conference Proceedings
%T MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering
%A Asano, Hikaru
%A Ouchi, Hiroki
%A Kasuga, Akira
%A Yonetani, Ryo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F asano-etal-2026-mobqa
%X This paper presents MobQA, a benchmark dataset designed to evaluate the semantic understanding capabilities of large language models (LLMs) for human mobility data through natural language question answering. While existing models predict human movement patterns well, their ability to interpret underlying reasons or semantic meaning remains unclear. To address this gap, we annotated 14,260 trajectory points from the GeoLife dataset with 30,703 semantic labels covering points of interest, mobility modes, and visit reasons, grounding semantics-dependent questions and reference answers. Building on these annotations, MobQA provides 5,800 question–answer pairs from daily and weekly GPS trajectories, spanning factual retrieval, multiple-choice semantic inference, and free-form interpretive explanation. Our evaluation of major LLMs reveals strong performance on factual retrieval but notable limits in semantic reasoning and explanation, with trajectory length substantially impacting effectiveness. These findings demonstrate both achievements and limitations of recent LLMs for semantic mobility understanding.
%U https://aclanthology.org/2026.emnlp-main.2223/
%P 50602-50633
Markdown (Informal)
[MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering](https://aclanthology.org/2026.emnlp-main.2223/) (Asano et al., EMNLP 2026)
ACL