4 citations · 14 across the 14 of their papers we have counts for
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Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
Tianqi Zhao, Russa Biswas, Megha Khosla
Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. St…
InFact: Informativeness Alignment for Improved LLM Factuality
Roi Cohen, Russa Biswas, Gerard de Melo
Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence ``Barack Obama was born in the Uni…
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
Ernests Lavrinovics, Russa Biswas, Katja Hose +1
Large Language Models (LLMs) have inherent limitations of faithfulness and factuality, commonly referred to as hallucinations. Several benchmarks have been developed that provide a…
Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality
Mike Zhang, Johannes Bjerva, Russa Biswas
We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and grounding them in knowledge grap…