# Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP

arXiv: https://arxiv.org/abs/2610.14101 · HTML: https://mlsanity.com/paper/2610.14101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [GREEK-BERT: The Greeks visiting Sesame Street](https://mlsanity.com/paper/2008.12014/) (2020)
- [Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency](https://mlsanity.com/paper/2403.18152/) (2024)
- [Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance](https://mlsanity.com/paper/2502.18772/) (2025)
- [Construction of a Japanese Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2403.15062/) (2024)
- [Financial Named Entity Recognition: How Far Can LLM Go?](https://mlsanity.com/paper/2501.02237/) (2025)
- [RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data](https://mlsanity.com/paper/2404.07452/) (2024)
- [FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks](https://mlsanity.com/paper/2604.10015/) (2026)
- [Training LayoutLM from Scratch for Efficient Named-Entity Recognition in the Insurance Domain](https://mlsanity.com/paper/2412.09341/) (2024)
- [Evaluation and Benchmarking Suite for Financial Large Language Models and Agents](https://mlsanity.com/paper/2602.19073/) (2026)
- [AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification](https://mlsanity.com/paper/2606.03031/) (2026)
- [Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals](https://mlsanity.com/paper/2607.12233/) (2026)

## Abstract (arXiv)

We present Fin_Agent, our submission to the FinEval 2026 shared task on multilingual fi nancial language understanding. Our sys tems address Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial ques tion answering. For Greek NER, we com bine annotation-aware numeric rules with zero shot Qwen2.5-32B classification, achieving 0.9521 public F1 and 0.8884 private F1. For Japanese implicit communication, an inter pretable cue-based ordinal scorer achieves 98.0% test accuracy. For multilingual financial QA, BGE-M3retrieval with Qwen3-32B gen eration achieves 0.3023 and 0.3419 ROUGE-1 F1 on the public and private splits. Key limita tions include dataset-specific rules, handcrafted cues, and limited supervision. Implementa tion code is available https://github.com/ Rashidnira/finagent-fineval-2026.