256 citations · 349 across the 25 of their papers we have counts for
13 papers · 1 filter
DocFusion: A Unified Framework for Document Parsing Tasks
Mingxu Chai, Ziyu Shen, Chong Zhang +6
Document parsing is essential for analyzing complex document structures and extracting fine-grained information, supporting numerous downstream applications. However, existing meth…
Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
Zhiheng Xi, Dingwen Yang, Jixuan Huang +21
Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as scie…
Aligning Large Language Models from Self-Reference AI Feedback with one General Principle
Rong Bao, Rui Zheng, Shihan Dou +6
In aligning large language models (LLMs), utilizing feedback from existing advanced AI rather than humans is an important method to scale supervisory signals. However, it is highly…
SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance
Caishuang Huang, Wanxu Zhao, Rui Zheng +11
As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. How…
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
Yuming Yang, Wantong Zhao, Caishuang Huang +11
Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs). Recent stu…
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
Xiao Wang, Tianze Chen, Xianjun Yang +3
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on…
EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
Weikang Zhou, Xiao Wang, Limao Xiong +18
Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibi…
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
Zhiheng Xi, Wenxiang Chen, Boyang Hong +18
In this paper, we propose R: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the bene…
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
Huijie Lv, Xiao Wang, Yuansen Zhang +6
Adversarial misuse, particularly through `jailbreaking' that circumvents a model's safety and ethical protocols, poses a significant challenge for Large Language Models (LLMs). Thi…
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions
Yuansen Zhang, Xiao Wang, Zhiheng Xi +4
Large Language Models (LLMs) have showcased remarkable capabilities in following human instructions. However, recent studies have raised concerns about the robustness of LLMs when…
Navigating the OverKill in Large Language Models
Chenyu Shi, Xiao Wang, Qiming Ge +7
Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer beni…
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Binghai Wang, Rui Zheng, Lu Chen +24
Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more hel…
Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback
Songyang Gao, Qiming Ge, Wei Shen +9
The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, trad…