← Back to all news
arXiv Computation and Language By Yan Lin, Jingyu Sun, Zhongliang Guo, Qing Li, Zhuohan Xie, Yuxia Wang

FinExam-10K: When Retrieval Helps Financial Reasoning?

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Computation and Language
Aug 27

FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review

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.

By Suyang Zhong, Jingzhe Zhu, Qi Xu, Liyao Sun, Yin Wang, Qingqing Sun, Shuai Chen, Tianyi Zhang
llmsbenchmarks
More like this →
arXiv AI
Aug 11

FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks

arXiv:2604. 10015v3 Announce Type: replace Abstract: Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon financial tasks.

By Yupeng Cao, Haohang Li, Weijin Liu, Wenbo Cao, Anke Xu, Lingfei Qian, Xueqing Peng, Minxue Tang, Zhiyuan Yao, Jimin Huang, K. P. Subbalakshmi, Zining Zhu, Jordan W. Suchow, Yangyang Yu
llmsagentsreinforcement-learningfine-tuningbenchmarks
More like this →