Papers by Marinka Zitnik
Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval (2026.acl-long)
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Joaquin Polonuer, Lucas Vittor, Iñaki Arango, Ayush Noori, David A. Clifton, Luciano Del Corro, Marinka Zitnik
| Challenge: | ARK: Adaptive Retriever of Knowledge is a tool-using KG retriever that allows a language model to control breadth-depth tradeoffs without requiring a fragile seed selection or pre-set hop depth. |
| Approach: | They propose a tool-using KG retriever that gives a language model control over breadth-depth tradeoff using global lexical search over node descriptors and one-hop neighborhood exploration that composes into multi-hop traversal. |
| Outcome: | The proposed model improves on a teacher's dataset by +7.0, +26.6, and +13.5% while retaining 98.5% of the teacher' s Hit@1 rate. |
MoExtend: Tuning New Experts for Modality and Task Extension (2024.acl-srw)
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| Challenge: | Existing instruction tuning methods for large language models (LLMs) are costly and difficult to implement. |
| Approach: | They propose a framework to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models. |
| Outcome: | The proposed framework enables rapid adaptation and extension to new modal data or tasks without tuning pretrained models. |