4 citations · 6 across the 10 of their papers we have counts for
10 papers
PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning
Mengdi Li, Guanqiao Chen, Xufeng Zhao +3
Reward models (RMs), which are central to existing post-training methods, aim to align LLM outputs with human values by providing feedback signals during fine-tuning. However, exis…
Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models
Liangyu Wang, Huanyi Xie, Xinhai Wang +3
Group-based reinforcement learning algorithms such as Group Reward Policy Optimization (GRPO) have proven effective for fine-tuning large language models (LLMs) with human feedback…
Understanding the Repeat Curse in Large Language Models from a Feature Perspective
Junchi Yao, Shu Yang, Jianhua Xu +3
Large language models (LLMs) have made remarkable progress in various domains, yet they often suffer from repetitive text generation, a phenomenon we refer to as the "Repeat Curse"…
Towards User-level Private Reinforcement Learning with Human Feedback
Jiaming Zhang, Mingxi Lei, Meng Ding +5
Reinforcement Learning with Human Feedback (RLHF) has emerged as an influential technique, enabling the alignment of large language models (LLMs) with human preferences. Despite th…
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation
Tong Li, Shu Yang, Junchao Wu +6
We present a comprehensive evaluation framework for assessing Large Language Models' (LLMs) capabilities in suicide prevention, focusing on two critical aspects: the Identification…
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements
Shu Yang, Shenzhe Zhu, Zeyu Wu +7
We introduce Fraud-R1, a benchmark designed to evaluate LLMs' ability to defend against internet fraud and phishing in dynamic, real-world scenarios. Fraud-R1 comprises 8,564 fraud…
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
Lijie Hu, Liang Liu, Shu Yang +5
Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabiliti…
Exploring the Personality Traits of LLMs through Latent Features Steering
Shu Yang, Shenzhe Zhu, Liang Liu +3
Large language models (LLMs) have significantly advanced dialogue systems and role-playing agents through their ability to generate human-like text. While prior studies have shown…
A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning
Lijie Hu, Liang Liu, Shu Yang +6
Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs). While some studies focus on improving CoT accuracy throug…
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
Shu Yang, Jiayuan Su, Han Jiang +5
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While exi…