2 citations · 3 across the 12 of their papers we have counts for
13 papers
REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems
Zixing Chen, Xingyuan Liu, Jie Zhu +6
Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks. Adversarial inputs can exploit interactions between the agent and i…
Dual-Loop Self-Evolution via Verifiable Emotion Feedback for Multi-Turn Empathetic Dialogue
Yi Wei, Shuo Jiang, Huaixia Dou +5
Large language models have demonstrated conversational capabilities, yet empathetic competence remains challenging. Empathetic support is inherently multi-turn and path-dependent:…
FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions
Huaixia Dou, Jie Zhu, Minghao Wu +5
As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consu…
ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations
Jie Zhu, Huaixia Dou, Shuo Jiang +5
Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited interpretability and little sup…
FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol
Jie Zhu, Yimin Tian, Boyang Li +8
This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of finan…
Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation
Ying Li, Xinglin Lyu, Junhui Li +5
Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextual signals are unevenly beneficia…
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation
Jie Zhu, Yuanchen Zhou, Shuo Jiang +5
Emotional Support Conversation (ESC) plays a vital role in alleviating psychological stress and providing emotional value through dialogue. While recent studies have largely focuse…
Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models
Jie Zhu, Yuanchen Zhou, Shuo Jiang +4
Process Reward Models (PRMs) supervise intermediate reasoning steps in large language models (LLMs), but existing PRMs are mainly trained on general-domain data and struggle with t…
Evaluating, Synthesizing, and Enhancing for Customer Support Conversation
Jie Zhu, Huaixia Dou, Junhui Li +4
Effective customer support requires not only accurate problem solving but also structured and empathetic communication aligned with professional standards. However, existing dialog…
MFinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset
Jie Zhu, Junhui Li, Yalong Wen +3
Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financ…
DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models
Jie Zhu, Qian Chen, Huaixia Dou +4
Effective reasoning remains a core challenge for large language models (LLMs) in the financial domain, where tasks often require domain-specific knowledge, precise numerical calcul…
Speech Translation Refinement using Large Language Models
Huaixia Dou, Xinyu Tian, Xinglin Lyu +3
Recent advancements in large language models (LLMs) have demonstrated their remarkable capabilities across various language tasks. Inspired by the success of text-to-text translati…
Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset
Jie Zhu, Junhui Li, Yalong Wen +1
In light of recent breakthroughs in large language models (LLMs) that have revolutionized natural language processing (NLP), there is an urgent need for new benchmarks to keep pace…