Position within the page tree

Home
Institute
Team
He, Yunjie

Yunjie He

Researcher
KI
Analytic Computing

Contact

+49 711 685 88126
Email

Universitätsstraße 32
70569 Stuttgart
Germany
Room: 2.312a

Publications

Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory. In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems. https://arxiv.org/abs/2312.14877
- BibTeX
- Link
BibTeX
@inproceedings{noauthororeditor2023robust, abstract = {Large-language models (LLMs) have the potential to support a wide range of applications like conversational agents, creative writing, text improvement, and general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they generate answers at random and their answers are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.}, author = {Potyka, Nico and Zhu, Yuqicheng and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems}, title = {Robust Knowledge Extraction from Large Language Models using Social Choice Theory}, url = {https://arxiv.org/abs/2312.14877}, year = 2024 }
Link
https://arxiv.org/abs/2312.14877
He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering?
- BibTeX
BibTeX
@inproceedings{he2023pattern, abstract = {Logical patterns, such as symmetry and composition, have been proven to be beneficial in the knowledge graph completion task. However, their influence has been unexplored in first-order logical (FOL) query reasoning methods. In this work, we present an inductive bias for query embedding models, Patternaware Cone Embedding (PConE), to support learning and reasoning with logical patterns. PConE combines the advantages of cones and the rotation operator for powerful algebraic operations for pattern inference. Our experiments demonstrate how the capability to capture logical patterns positively impacts the results of query answering.}, author = {He, Yunjie and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, title = {Can Pattern Learning Enhance Complex Logical Query Answering?}, venue = {Thhe 22nd International Semantic Web Conference (ISWC 2023)}, year = 2023 }

Teaching

Deep Learning Lab 2022/23 Winter Term

Yunjie He

Contact

Publications

BibTeX

Link

BibTeX

Teaching

Audience

Formalities

Services

Organization

Yunjie He

Contact

Publications

BibTeX

Link

BibTeX

Teaching

Here you can reach us

Audience

Formalities

Services

Organization