K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2002.01808v5)

Abstract: We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away. To address this, we propose K-Adapter, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model. Taking RoBERTa as the backbone model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus multiple adapters can be efficiently trained in a distributed way. As a case study, we inject two kinds of knowledge in this work, including (1) factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata and (2) linguistic knowledge obtained via dependency parsing. Results on three knowledge-driven tasks, including relation classification, entity typing, and question answering, demonstrate that each adapter improves the performance and the combination of both adapters brings further improvements. Further analysis indicates that K-Adapter captures versatile knowledge than RoBERTa.

• The paper introduces a novel adapter-based framework that infuses both factual and linguistic knowledge while preserving the core parameters of pre-trained models.
• It employs independent neural adapters that integrate with intermediate hidden states, enhancing tasks such as relation classification and entity typing.
• Experimental results demonstrate significant gains in mean F1 scores and overall performance on complex tasks including open-domain question answering.

Infusing Knowledge into Pre-Trained Models with K-Adapter

In contemporary natural language processing research, large pre-trained models such as BERT and RoBERTa have excelled in various downstream tasks. However, these models often struggle to encode rich, domain-specific knowledge efficiently. The paper "K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters" addresses this limitation by introducing a novel framework to seamlessly incorporate factual and linguistic knowledge without disrupting the core model parameters.

Methodology Overview

The primary contribution of this research is the introduction of K-Adapter, a method for injecting multiple types of knowledge into pre-trained models using compact neural models known as adapters. Unlike traditional approaches that adjust the entire pre-trained model's parameters, K-Adapter retains the original parameters, thus preserving previously learned knowledge and allowing for efficient training and continual knowledge infusion.

K-Adapter operates by integrating adapters as independent modules outside the main model architecture, each dedicated to a specific type of knowledge. These adapters interact with the pre-trained model's intermediate hidden states without influencing other adapters or the core parameters. The paper demonstrates the approach using RoBERTa as the backbone, with two specific knowledge types injected: factual knowledge from Wikipedia and Wikidata, and linguistic knowledge derived from dependency parsing.

Experimental Results

The efficacy of K-Adapter is tested across several knowledge-driven tasks, including relation classification, entity typing, and question answering. In the field of relation classification, the model significantly outperforms baselines such as BERT and existing methods like KnowBERT, showcasing its ability to retain and apply factual knowledge. For instance, K-Adapter achieves an impressive improvement in mean F1 scores, surpassing other contemporaneous approaches.

In entity typing, K-Adapter's architecture demonstrates the adaptability and scalability of incorporating multiple knowledge sources. It successfully leverages both factual and linguistic knowledge to outperform state-of-the-art models on datasets like OpenEntity and FIGER.

The paper also evaluates K-Adapter in open-domain and commonsense question answering contexts. Here, again, the model shows superior performance compared to baseline systems, reflecting its enhanced ability to reason and infer from complex datasets.

Implications and Future Work

K-Adapter's architecture presents a significant step forward in knowledge integration for NLP models. The ability to independently pre-train multiple adapters allows for flexible, modular knowledge updates without retraining the entire model or encountering catastrophic forgetting, which is a limitation of previous knowledge-enhanced models.

In a broader sense, K-Adapter lays the groundwork for future research in dynamic knowledge infusion into pre-trained models. This approach aligns with the evolving needs of AI systems to remain adaptive and knowledgeable across diverse domains and applications. Future developments may explore integrating additional forms of knowledge or applying K-Adapter to other pre-trained architectural frameworks beyond RoBERTa.

Moreover, investigating the long-term trade-offs between parameter efficiency and information richness could yield optimizations in both training and inference efficiencies. The K-Adapter's promising results open avenues for applying such techniques to more abstract reasoning tasks prevalent in AI research.

In conclusion, the "K-Adapter" paper delineates a systematic methodology for enhancing pre-trained LLMs with domain-specific knowledge efficiently. By balancing architectural simplicity with knowledge multiplicity, K-Adapter sets a precedent for future innovations in knowledge-infused NLP initiatives.