1 citations · 1 across the 3 of their papers we have counts for
4 papers
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
Yash Saxena, Ankur Padia, Kalpa Gunaratna +1
Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is…
PathFinder: MCTS and LLM Feedback-based Path Selection for Multi-Hop Question Answering
Durga Prasad Maram, Kalpa Gunaratna, Vijay Srinivasan +2
Multi-hop question answering is a challenging task in which language models must reason over multiple steps to reach the correct answer. With the help of Large Language Models and…
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
Yash Saxena, Ankur Padia, Mandar S Chaudhary +3
Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards against data poisoning, yet current a…
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
Shuxin Li, Zixian Huang, Gong Cheng +2
A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and…