1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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The parametric knowledge memorized by large language models (LLMs) becomes outdated quickly.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…
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…
Learning node embeddings via summary graphs: a brief theoretical analysis
Houquan Zhou, Shenghua Liu, Danai Koutra +2
Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improv…
Marked Temporal Dynamics Modeling based on Recurrent Neural Network
Yongqing Wang, Shenghua Liu, Huawei Shen +1
We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark inform…