Papers with knowledge alignment
A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)
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| Challenge: | Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG. |
| Approach: | They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models . |
| Outcome: | The proposed pipeline can enhance the performance of conventional KGR models in incomplete and general situations. |
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models often ignore external knowledge to generate accurate answers . despite correct groundings, they can rely on wrong grounding or biases to hallucinate . |
| Approach: | They propose a framework that integrates human and human user clarifications to improve knowledge alignment. |
| Outcome: | The proposed framework improves model performance and mitigates hallucination by producing user-centered clarifications. |
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are pre-trained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. |
| Approach: | They propose to use the **C**ross-Lingual Self-**Aligning ability of **L**anguage **M**odels to align knowledge across languages. |
| Outcome: | The proposed model performs well in both zero-shot and retrieval-augmented settings. |
KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)
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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals. |
| Approach: | They propose a self-supervised framework that enhances RAG systems through efficient model adaptation. |
| Outcome: | The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models. |