1 citations · 1 across the 6 of their papers we have counts for
6 papers
DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering
Xinyi Wang, Yiping Song, Zhiliang Tian +3
In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Kn…
RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models
Yue Zhang, Zhiliang Tian, Shicheng Zhou +7
Legal Judgment Prediction (LJP) is a pivotal task in legal AI. Existing semantic-enhanced LJP models integrate judicial precedents and legal knowledge for high performance. But the…
DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak
Hao Wang, Hao Li, Junda Zhu +4
Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become mo…
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen, Zhiliang Tian, Zexin Jian +5
Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. The knowledge boundary (KB) of an LLM limits its factual understanding, beyond wh…
360REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
Shen Gao, Hao Li, Chengrui Huang +4
Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to…
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification
Yiping Song, Juhua Zhang, Zhiliang Tian +3
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmen…