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20232026
most citedFinBen: A Holistic Financial Benchmark for Large Language Models

21 citations · 61 across the 19 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CL2025

When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents

Lingfei Qian, Xueqing Peng, Yan Wang +14

Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies t…

cs.CL2025

Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim Evidence Reasoning

Shashidhar Reddy Javaji, Yupeng Cao, Haohang Li +3

Large language models (LLMs) are increasingly being used for complex research tasks such as literature review, idea generation, and scientific paper analysis, yet their ability to…

cs.CL2025

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application

Xueqing Peng, Lingfei Qian, Yan Wang +44

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…

cs.CL2025

Truth Neurons

Haohang Li, Yupeng Cao, Yangyang Yu +2

Despite their remarkable success and deployment across diverse workflows, language models sometimes produce untruthful responses. Our limited understanding of how truthfulness is m…

cs.CE2025

FinAudio: A Benchmark for Audio Large Language Models in Financial Applications

Yupeng Cao, Haohang Li, Yangyang Yu +10

Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and…

cs.AI2025

FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading

Guojun Xiong, Zhiyang Deng, Keyi Wang +10

Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle wi…