1 citations · 1 across the 8 of their papers we have counts for
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Plug-and-Play Training Framework for Preference Optimization
Jingyuan Ma, Rui Li, Zheng Li +2
Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, cur…
Large Language Models Struggle with Unreasonability in Math Problems
Jingyuan Ma, Damai Dai, Zihang Yuan +6
Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonab…
Rethinking Node-wise Propagation for Large-scale Graph Learning
Xunkai Li, Jingyuan Ma, Zhengyu Wu +4
Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale…
ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors
Zhexin Zhang, Yida Lu, Jingyuan Ma +8
The safety of Large Language Models (LLMs) has gained increasing attention in recent years, but there still lacks a comprehensive approach for detecting safety issues within LLMs'…