Papers by Oliver Heinimann

1 papers
DocReRank: Single-Page Hard Negative Query Generation for Training Multi-Modal RAG Rerankers (2025.emnlp-main)

Copied to clipboard

Challenge: Existing models focus on identifying relevant documents, but embedding similarity often limits accuracy.
Approach: They propose a method to generate hard negative queries per page instead of negative pages per query . they propose to refine ranking of an initial set of retrieved documents using hard negative mining .
Outcome: The proposed approach outperforms existing models and significantly improves retrieval performance.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations