Papers by Qingkun Tang
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model’s Reasoning Path Aggregation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for large language models (LLMs) are limited by step-by-step decision-making on KGs, or require fine-tuning or pre-training on specific KG. |
| Approach: | They propose a framework that harnesses the global planning abilities of large language models (LLMs) for efficient and accurate KG reasoning. |
| Outcome: | Extensive experiments show that the proposed framework achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy. |