25 citations · 34 across the 4 of their papers we have counts for
4 papers
Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning
Jingyi Ren, Yekun Xu, Xiaolong Wang +4
Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that tr…
Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks
Daniel Nemirovsky, Nicolas Thiebaut, Ye Xu +1
Machine learning predictors have been increasingly applied in production settings, including in one of the world's largest hiring platforms, Hired, to provide a better candidate an…
CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets
Daniel Nemirovsky, Nicolas Thiebaut, Ye Xu +1
The prevalence of machine learning models in various industries has led to growing demands for model interpretability and for the ability to provide meaningful recourse to users. F…
Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn
Viet Ha-Thuc, Ye Xu, Satya Pradeep Kanduri +5
One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typical…