10 citations · 14 across the 8 of their papers we have counts for
8 papers
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios
Yutao Hou, Yihan Jiang, Yuhan Xie +5
Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical beha…
VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
Zhaowei Liu, Xin Guo, Haotian Xia +11
Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the…
FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain
Lingfeng Zeng, Fangqi Lou, Zixuan Wang +18
The booming development of AI agents presents unprecedented opportunities for automating complex tasks across various domains. However, their multi-step, multi-tool collaboration c…
FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning
Shaoyu Dou, Yutian Shen, Mofan Chen +9
Large Language Models (LLMs) demonstrate significant potential but face challenges in complex financial reasoning tasks requiring both domain knowledge and sophisticated reasoning.…
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
Zhaowei Liu, Xin Guo, Zhi Yang +14
In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their ap…
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Xin Guo, Haotian Xia, Zhaowei Liu +14
Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been…
Variance Control for Distributional Reinforcement Learning
Qi Kuang, Zhoufan Zhu, Liwen Zhang +1
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator…
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
Fan Zhou, Zhoufan Zhu, Qi Kuang +1
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address. One is how to…