| Challenge: | Large pre-trained language models retain implicit knowledge within their parameters, but are susceptible to memorizing the pretraining corpora rather than capturing the knowledge within them. |
| Approach: | They propose to inject entity-related knowledge into encoder-decoder PLMs via a generative knowledge infilling objective through continued pre-training. |
| Outcome: | The proposed approach outperforms state-of-the-art models on general NLU and NLG tasks while maintaining their original performance. |
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TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
What Has Been Enhanced in my Knowledge-Enhanced Language Model? (2022.findings-emnlp)
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| Challenge: | Existing knowledge integration methods such as linear probes and prompts have key limitations in answering these questions. |
| Approach: | They propose a new probe model which integrates external knowledge from knowledge graphs into pretrained language models (LMs) ERNIE and K-Adapter are proposed as KI methods . |
| Outcome: | The proposed model interprets two well-known KELMs using graph attention on the corresponding knowledge graph for interpretation. |
Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems (2022.emnlp-main)
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| Challenge: | Pre-trained language models lack domain-specific knowledge that does not naturally occur in pre-training data. |
| Approach: | They propose to inject domain-specific knowledge prior to fine-tuning on TOD tasks by using adapters that can be easily integrated with PLMs. |
| Outcome: | The proposed methods show that they can inject domain-specific knowledge prior to fine-tuning on TOD tasks. |
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge (2023.acl-long)
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| Challenge: | Existing methods for updating knowledge show little propagation of injected knowledge. |
| Approach: | They propose to inject individual facts into LMs and evaluate whether they can propagate injected facts while not changing predictions on other contexts. |
| Outcome: | The proposed model can make inferences based on injected facts and propagate them . existing methods show little propagation of injected knowledge . |
Improving Pre-trained Language Models with Knowledge Enhancement and Filtering Framework (2025.findings-naacl)
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| Challenge: | Existing knowledge enhancement techniques for pre-trained language models (PLMs) introduce noisy entity representations. |
| Approach: | They propose a knowledge enhancement filter that integrates external knowledge bases to enhance PLMs' ability to capture entity knowledge. |
| Outcome: | The proposed method achieves the highest F1-score and accuracy while reducing the computational cost by 1.7-2.5x. |
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding (2023.acl-long)
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| Challenge: | Existing approaches to infuse knowledge graphs with pre-trained LMs are limited by the input sequence length. |
| Approach: | They propose a language model that leverages knowledge in local, document-level, and global contexts for long document understanding. |
| Outcome: | The proposed model achieves state-of-the-art on three long document understanding tasks across 6 datasets/settings. |
Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)
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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases. |
| Approach: | They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. |
| Outcome: | The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen. |
Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition (N19-1)
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| Challenge: | Current language models are unable to efficiently model entity names observed in text providing insufficient context. |
| Approach: | They propose to augment a traditional model with an external knowledge base to model entity names observed in text. |
| Outcome: | The proposed model improves on a Named Entity Recognition (NER) task by requiring no additional information such as named entity tags. |
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method (2022.coling-1)
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| Challenge: | Recent models rely on pretrained language models that use metadata as inputs . however, these methods are either nontrivial or cost-ineffective . |
| Approach: | They propose a benchmark for evaluating attribute injection models using eight datasets . they extend adapters to include attributes independently of or jointly with the text . |
| Outcome: | The proposed method outperforms previous methods and achieves state-of-the-art performance on all datasets. |