@inproceedings{mohamed-ajlan-2026-al-qahera,
title = "Al-Qahera at the {F}in{E}val 2026: {GEPA} Prompt Evolution for {G}reek Financial {NER} and {J}apanese Implicit Communication Recognition",
author = "Mohamed, Mohamed and
Ajlan, Hazem",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.75/",
pages = "829--834",
ISBN = "979-8-89176-466-8",
abstract = "We describe the Al-Qahera submission to two subtasks of the FinNLP 2026 Multilin- gual Financial Language Understanding Chal- lenge: Greek financial numeric named-entity recognition (GRFinNUM) and Japanese im- plicit communication recognition (JF-ICR). For both subtasks we optimize a single frozen task model with GEPA, a reflective, natural- language prompt evolution method, using a smaller model as reflection critic. GEPA raises held-out numeric-entity F1 from a 0.473 seed to 0.978, and raises Japanese intent-classification accuracy from a 0.38 seed to 0.692, in both cases without any change to the task model it- self. As a secondary exploration on Subtask 1, we also try Meta-Harness, a structurally differ- ent optimizer that searches over the surround- ing program instead of the prompt; it is not part of our submitted predictions, but it surfaces a concrete case of search-set overfitting that we report as a finding in its own right."
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<abstract>We describe the Al-Qahera submission to two subtasks of the FinNLP 2026 Multilin- gual Financial Language Understanding Chal- lenge: Greek financial numeric named-entity recognition (GRFinNUM) and Japanese im- plicit communication recognition (JF-ICR). For both subtasks we optimize a single frozen task model with GEPA, a reflective, natural- language prompt evolution method, using a smaller model as reflection critic. GEPA raises held-out numeric-entity F1 from a 0.473 seed to 0.978, and raises Japanese intent-classification accuracy from a 0.38 seed to 0.692, in both cases without any change to the task model it- self. As a secondary exploration on Subtask 1, we also try Meta-Harness, a structurally differ- ent optimizer that searches over the surround- ing program instead of the prompt; it is not part of our submitted predictions, but it surfaces a concrete case of search-set overfitting that we report as a finding in its own right.</abstract>
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%0 Conference Proceedings
%T Al-Qahera at the FinEval 2026: GEPA Prompt Evolution for Greek Financial NER and Japanese Implicit Communication Recognition
%A Mohamed, Mohamed
%A Ajlan, Hazem
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F mohamed-ajlan-2026-al-qahera
%X We describe the Al-Qahera submission to two subtasks of the FinNLP 2026 Multilin- gual Financial Language Understanding Chal- lenge: Greek financial numeric named-entity recognition (GRFinNUM) and Japanese im- plicit communication recognition (JF-ICR). For both subtasks we optimize a single frozen task model with GEPA, a reflective, natural- language prompt evolution method, using a smaller model as reflection critic. GEPA raises held-out numeric-entity F1 from a 0.473 seed to 0.978, and raises Japanese intent-classification accuracy from a 0.38 seed to 0.692, in both cases without any change to the task model it- self. As a secondary exploration on Subtask 1, we also try Meta-Harness, a structurally differ- ent optimizer that searches over the surround- ing program instead of the prompt; it is not part of our submitted predictions, but it surfaces a concrete case of search-set overfitting that we report as a finding in its own right.
%U https://aclanthology.org/2026.finnlp-1.75/
%P 829-834
Markdown (Informal)
[Al-Qahera at the FinEval 2026: GEPA Prompt Evolution for Greek Financial NER and Japanese Implicit Communication Recognition](https://aclanthology.org/2026.finnlp-1.75/) (Mohamed & Ajlan, FinNLP 2026)
ACL