1 citations · 1 across the 7 of their papers we have counts for
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Language Models as Continuous Self-Evolving Data Engineers
Peidong Wang, Ming Wang, Zhiming Ma +5
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addit…
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
Ming Wang, Yuanzhong Liu, Xiaoyu Liang +8
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI expe…
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
Xiaoming Zhang, Ming Wang, Xiaocui Yang +3
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems encounte…
MM-InstructEval: Zero-Shot Evaluation of (Multimodal) Large Language Models on Multimodal Reasoning Tasks
Xiaocui Yang, Wenfang Wu, Shi Feng +7
The emergence of multimodal large language models (MLLMs) has triggered extensive research in model evaluation. While existing evaluation studies primarily focus on unimodal (visio…
Is Mamba Effective for Time Series Forecasting?
Zihan Wang, Fanheng Kong, Shi Feng +5
In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future stat…
FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models
Huaiwen Zhang, Yu Chen, Ming Wang +1
Emotional Support Conversation (ESC) is a typical dialogue that can effectively assist the user in mitigating emotional pressures. However, owing to the inherent subjectivity invol…