arXiv:2607. 19856v1 Announce Type: cross Abstract: FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi.
arXiv:2607. 19867v1 Announce Type: cross Abstract: FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence.
arXiv:2607. 22841v1 Announce Type: cross Abstract: We present DS@GT's submission to FinMMEval 2026 Task 1, a multilingual financial exam question answering benchmark spanning English, Spanish, Greek, Chinese, and Hindi.
The IGT system tackles PolyFiQA Task 2 of the FinMMEval Lab, a multilingual financial QA challenge involving English SEC filings and news in five languages. It distinguishes two question families: numeric‑structured queries are answered via keyword extraction from filings, while synthesis queries use rule‑based passage selection from news. The approach yields a development ROUGE‑1 of ~0.395, a 60% boost over a generic RAG baseline, and places third among twelve teams on the official test set.
FinRAG-QA is a new benchmark dataset for financial question answering, featuring 999 practitioner-curated questions on 10 standardised indicators drawn from 209 annual and Pillar 3 reports of 24 major European and U.S. banks between 2019 and 2023. The dataset focuses on cross‑institutional retrieval over documents averaging 198k words, making it longer than any existing financial QA resource. Experiments on a multi‑stage Retrieval‑Augmented Generation pipeline show that contextual chunk enrichment and a retrieval‑optimised embedding model significantly improve NDCG@10, while a reasoning‑optimised generator boosts answer accuracy from 44.6% to 79.0% when the correct document is retrieved.
arXiv:2609.24002v1 Announce Type: new Abstract: Large language model agents increasingly answer financial questions by searching regulatory filings. Such questions are often deceptively under-specifi...
FinRiskAtlas is a Chinese-language benchmark designed to evaluate large language models (LLMs) for financial risk review by focusing on decision‑aligned tasks rather than generic financial knowledge. It contains 9,742 instances across 53 task families, including 42 domain‑knowledge families and 11 downstream review operations defined by explicit evaluation contracts. The extended FinRisk‑Ask framework replays 680 pre‑action states from 104 professional trajectories, withholding future evidence during inference to assess evidence‑state control and request targeting. Results across 33 model configurations show that operation‑level evaluation yields distinct rankings and that knowledge‑based shortlisting can incur significant regret, while frequent use of the Ask branch does not necessarily improve evidence acquisition, highlighting gaps in broad financial capability scores.
arXiv:2606. 07167v1 Announce Type: cross Abstract: Meaningful multilingual evaluation must test models in the target language and educational context.
arXiv:2608. 04374v1 Announce Type: cross Abstract: Large language models can produce fluent financial analysis, but fluency alone does not establish whether a report is suitable for institutional delivery.
FinExam-10K is a new English benchmark for financial reasoning, comprising 10,198 expert‑reannotated questions covering CFA Levels I‑III and FRM Parts I‑II. The dataset is split into a 5,110‑question release and a 5,088‑question held‑out set for a quarterly leaderboard, with separate Full‑Coverage and Context‑Complete Reasoning tracks. Across 17 models, the best overall accuracy is 85.29 %, but performance drops on harder subsets, and retrieval‑augmented methods like Function‑Graph‑RAG provide modest gains when gated appropriately.
VākQA is a newly introduced benchmark for Telugu spoken factoid question answering, comprising 2,001 question‑answer pairs across six domains, 2.53 hours of speech audio, bilingual transcriptions, and human‑verified reference answers. The study validates automatic evaluation methods against human judgments, finding that Gemini‑as‑a‑judge best approximates human ratings but is inconsistently strict, while open‑weight judges tend to penalize correct Telugu answers that differ in surface form. Using this validated setup, the authors benchmark proprietary and open‑weight models, highlighting challenges such as cultural specificity loss in translation, phonetic confusions from speech input, and compounded errors from cascaded ASR‑MT pipelines.
arXiv:2604. 04532v2 Announce Type: replace-cross Abstract: Evaluation language is typically treated as a fixed English default in agentic code benchmarks, yet we show that changing the judge's language can invert backbone rankings.
arXiv:2608. 08634v1 Announce Type: new Abstract: Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months.