@inproceedings{razvan-2026-mircea,
title = "Mircea Andrei Razvan at the {F}in{E}val 2026: A Unified Prompting Approach for Multilingual Financial {Q}{\&}{A}",
author = "Razvan, Mircea Andrei",
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-1.69/",
pages = "778--782",
ISBN = "979-8-89176-466-8",
abstract = "We describe our submission to Subtask 3 of the FinNLP 2026 Shared Task, based on MultiFinBen/PolyFiQA (Peng et al., 2026), in which models answer cross-document financial questions using SEC financial-report statements and contemporaneous multilingual news in English, Chinese, Japanese, Spanish, and Greek, evaluated using ROUGE-1. The task{'}s four fixed question templates require an answer synthesized from the news, supported by evidence cited from the financial statements. We use Qwen3-VL-30B-A3B-Instruct, an open-weight mixture-of-experts model accessed via OpenRouter, with template-aware prompting for each question type. In early development, a multi-stage pipeline separating answer generation from evidence retrieval produced a high rate of empty evidence fields. A single unified generation call substantially reduced this rate. We additionally find that several formatting conventions, including specific numeric notation, compact period references, and a fixed field label, were not reliably followed through prompt instructions alone, and were instead enforced with rule-based post-processing. Our final system achieves a ROUGE-1 F1 of 36.53{\%} on the official test set. These results suggest that deterministic post-processing remains a practical complement to prompt-based instruction for enforcing output conventions that models do not reliably learn from context alone."
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<abstract>We describe our submission to Subtask 3 of the FinNLP 2026 Shared Task, based on MultiFinBen/PolyFiQA (Peng et al., 2026), in which models answer cross-document financial questions using SEC financial-report statements and contemporaneous multilingual news in English, Chinese, Japanese, Spanish, and Greek, evaluated using ROUGE-1. The task’s four fixed question templates require an answer synthesized from the news, supported by evidence cited from the financial statements. We use Qwen3-VL-30B-A3B-Instruct, an open-weight mixture-of-experts model accessed via OpenRouter, with template-aware prompting for each question type. In early development, a multi-stage pipeline separating answer generation from evidence retrieval produced a high rate of empty evidence fields. A single unified generation call substantially reduced this rate. We additionally find that several formatting conventions, including specific numeric notation, compact period references, and a fixed field label, were not reliably followed through prompt instructions alone, and were instead enforced with rule-based post-processing. Our final system achieves a ROUGE-1 F1 of 36.53% on the official test set. These results suggest that deterministic post-processing remains a practical complement to prompt-based instruction for enforcing output conventions that models do not reliably learn from context alone.</abstract>
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%0 Conference Proceedings
%T Mircea Andrei Razvan at the FinEval 2026: A Unified Prompting Approach for Multilingual Financial Q&A
%A Razvan, Mircea Andrei
%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 razvan-2026-mircea
%X We describe our submission to Subtask 3 of the FinNLP 2026 Shared Task, based on MultiFinBen/PolyFiQA (Peng et al., 2026), in which models answer cross-document financial questions using SEC financial-report statements and contemporaneous multilingual news in English, Chinese, Japanese, Spanish, and Greek, evaluated using ROUGE-1. The task’s four fixed question templates require an answer synthesized from the news, supported by evidence cited from the financial statements. We use Qwen3-VL-30B-A3B-Instruct, an open-weight mixture-of-experts model accessed via OpenRouter, with template-aware prompting for each question type. In early development, a multi-stage pipeline separating answer generation from evidence retrieval produced a high rate of empty evidence fields. A single unified generation call substantially reduced this rate. We additionally find that several formatting conventions, including specific numeric notation, compact period references, and a fixed field label, were not reliably followed through prompt instructions alone, and were instead enforced with rule-based post-processing. Our final system achieves a ROUGE-1 F1 of 36.53% on the official test set. These results suggest that deterministic post-processing remains a practical complement to prompt-based instruction for enforcing output conventions that models do not reliably learn from context alone.
%U https://aclanthology.org/2026.finnlp-1.69/
%P 778-782
Markdown (Informal)
[Mircea Andrei Razvan at the FinEval 2026: A Unified Prompting Approach for Multilingual Financial Q&A](https://aclanthology.org/2026.finnlp-1.69/) (Razvan, FinNLP 2026)
ACL