2 papers
cs.IR2026
Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
Yue Kang, Zhuoyi Huang, Benji Schussheim +19
In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an…
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…