5 citations · 8 across the 14 of their papers we have counts for
15 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:…
Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Haoran Ding, Wenlin Zhao, Yuchen Jiang +16
Large recommendation models have demonstrated substantial potential gains under scaling laws, yet these gains are difficult to realize in industrial recommendation systems because…
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…
Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations
Jie Zhu, Huaixia Dou, Junhui Li +5
Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each…
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…
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…
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu, Junhui Li, Muhua Zhu +3
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph…