@inproceedings{jin-etal-2026-lantern,
title = "{LANTERN} in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts",
author = "Jin, Chengyuan and
Chang, Ao and
Zeng, Daojian and
Teng, Wenhao and
Liao, Xiangwen and
Liu, Kang and
Zhao, Jun and
Chen, Yubo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.559/",
pages = "11519--11533",
ISBN = "979-8-89176-395-1",
abstract = "Temporal knowledge graph forecasting(TKGF) asks a model to rank the mostplausible future entity for a query such as(s, r, ?, t) from historical events. Recenttraining-free methods use large languagemodels (LLMs) for this task, but their accuracydepends heavily on which past events areshown in the prompt under a tight contextbudget. We present LANTERN, a training-freeprompting framework that addresses thisbottleneck by combining two complementaryviews of history: a long-window strengthscore for stable interaction patterns anda short-window novelty score for suddenchanges. LANTERN first filters unhelpfulevents, then selects a compact evidence setwith Pareto-greedy selection, and finally addsone structure-aware analogical demonstration.Across ICEWS14, ICEWS05-15, ICEWS18,and GDELT, LANTERN consistently outperforms the state-of-the-art training-free baselineAnRe under the same backbone and 2-hopcandidate protocol, improving Hits@1 by upto 2.5 points and MRR by up to 1.2 points."
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<abstract>Temporal knowledge graph forecasting(TKGF) asks a model to rank the mostplausible future entity for a query such as(s, r, ?, t) from historical events. Recenttraining-free methods use large languagemodels (LLMs) for this task, but their accuracydepends heavily on which past events areshown in the prompt under a tight contextbudget. We present LANTERN, a training-freeprompting framework that addresses thisbottleneck by combining two complementaryviews of history: a long-window strengthscore for stable interaction patterns anda short-window novelty score for suddenchanges. LANTERN first filters unhelpfulevents, then selects a compact evidence setwith Pareto-greedy selection, and finally addsone structure-aware analogical demonstration.Across ICEWS14, ICEWS05-15, ICEWS18,and GDELT, LANTERN consistently outperforms the state-of-the-art training-free baselineAnRe under the same backbone and 2-hopcandidate protocol, improving Hits@1 by upto 2.5 points and MRR by up to 1.2 points.</abstract>
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%0 Conference Proceedings
%T LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts
%A Jin, Chengyuan
%A Chang, Ao
%A Zeng, Daojian
%A Teng, Wenhao
%A Liao, Xiangwen
%A Liu, Kang
%A Zhao, Jun
%A Chen, Yubo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jin-etal-2026-lantern
%X Temporal knowledge graph forecasting(TKGF) asks a model to rank the mostplausible future entity for a query such as(s, r, ?, t) from historical events. Recenttraining-free methods use large languagemodels (LLMs) for this task, but their accuracydepends heavily on which past events areshown in the prompt under a tight contextbudget. We present LANTERN, a training-freeprompting framework that addresses thisbottleneck by combining two complementaryviews of history: a long-window strengthscore for stable interaction patterns anda short-window novelty score for suddenchanges. LANTERN first filters unhelpfulevents, then selects a compact evidence setwith Pareto-greedy selection, and finally addsone structure-aware analogical demonstration.Across ICEWS14, ICEWS05-15, ICEWS18,and GDELT, LANTERN consistently outperforms the state-of-the-art training-free baselineAnRe under the same backbone and 2-hopcandidate protocol, improving Hits@1 by upto 2.5 points and MRR by up to 1.2 points.
%U https://aclanthology.org/2026.findings-acl.559/
%P 11519-11533
Markdown (Informal)
[LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts](https://aclanthology.org/2026.findings-acl.559/) (Jin et al., Findings 2026)
ACL
- Chengyuan Jin, Ao Chang, Daojian Zeng, Wenhao Teng, Xiangwen Liao, Kang Liu, Jun Zhao, and Yubo Chen. 2026. LANTERN in the Event Stream: Training-Free Temporal Knowledge Graph Forecasting by Balancing Inertia and Shifts. In Findings of the Association for Computational Linguistics: ACL 2026, pages 11519–11533, San Diego, California, United States. Association for Computational Linguistics.