Papers by Xiou Ge
GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)
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| Challenge: | Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs). |
| Approach: | They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function. |
| Outcome: | Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size. |
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
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Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
Evaluating Evaluation Metrics – The Mirage of Hallucination Detection (2025.findings-emnlp)
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Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz, Xiou Ge, Bo-Hsiang Tseng, Dhivya Piraviperumal, Swabha Swayamdipta, Hong Yu
| Challenge: | a large-scale empirical evaluation of hallucination detection metrics is conducted . hallucinosity is a significant obstacle to the reliability and widespread adoption of language models . |
| Approach: | They conduct large-scale empirical evaluation of hallucination detection metrics . they compare hallucinian language models, language models and decoding methods . |
| Outcome: | The results show that the evaluations of hallucination detection metrics fail to align with human judgments, they say . they also show that evaluations with LLM-based evaluation yield the best overall results . |
Compounding Geometric Operations for Knowledge Graph Completion (2023.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) is one of the most fundamental problems in AI research. |
| Approach: | They propose a new knowledge graph embedding model by leveraging translation, rotation, and scaling operations to form a composite one. |
| Outcome: | The proposed model outperforms existing models on three KG prediction tasks. |