3 papers
cs.CL2026
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
Taiwei Shi, Zhuoer Wang, Longqi Yang +12
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on hum…
cs.LG2024
Rethinking Node Representation Interpretation through Relation Coherence
Ying-Chun Lin, Jennifer Neville, Cassiano Becker +3
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on expla…
cs.LG2024
Improving Node Representation by Boosting Target-Aware Contrastive Loss
Ying-Chun Lin, Jennifer Neville
Graphs model complex relationships between entities, with nodes and edges capturing intricate connections. Node representation learning involves transforming nodes into low-dimensi…