13 citations · 29 across the 37 of their papers we have counts for
10 papers · 1 filter
Context-DPO: Aligning Language Models for Context-Faithfulness
Baolong Bi, Shaohan Huang, Yiwei Wang +11
Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intenti…
HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router
Lingrui Mei, Shenghua Liu, Yiwei Wang +3
As Large Language Models (LLMs) grow increasingly powerful, ensuring their safety and alignment with human values remains a critical challenge. Ideally, LLMs should provide informa…
StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
Baolong Bi, Shenghua Liu, Yiwei Wang +4
As the modern tool of choice for question answering, large language models (LLMs) are expected to deliver answers with up-to-date knowledge. To achieve such ideal question-answerin…
Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities
Baolong Bi, Shenghua Liu, Yiwei Wang +4
The parametric knowledge memorized by large language models (LLMs) becomes outdated quickly. In-context editing (ICE) is currently the most effective method for updating the knowle…
"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak
Lingrui Mei, Shenghua Liu, Yiwei Wang +3
"Jailbreak" is a major safety concern of Large Language Models (LLMs), which occurs when malicious prompts lead LLMs to produce harmful outputs, raising issues about the reliabilit…
Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts
Baolong Bi, Shenghua Liu, Lingrui Mei +3
The knowledge within large language models (LLMs) may become outdated quickly. While in-context editing (ICE) is currently the most effective method for knowledge editing (KE), it…
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
Baolong Bi, Shenghua Liu, Yiwei Wang +5
As the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context. This re…
LPNL: Scalable Link Prediction with Large Language Models
Baolong Bi, Shenghua Liu, Yiwei Wang +2
Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significa…
Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?
Yuyao Ge, Shenghua Liu, Baolong Bi +5
Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph proble…
SLANG: New Concept Comprehension of Large Language Models
Lingrui Mei, Shenghua Liu, Yiwei Wang +2
The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs).…