insight - NLP Research - # Knowledge Infusion in LLMs

Infusing Knowledge into Large Language Models with Contextual Prompts: A Detailed Study

Q: How can the proposed method be adapted for applications where external knowledge graphs are available?

In scenarios where external knowledge graphs are accessible, the proposed method of using contextual prompts for knowledge infusion in Large Language Models (LLMs) can be enhanced by integrating information from these knowledge graphs. By incorporating data from external sources, such as structured knowledge bases or domain-specific repositories, alongside the contextual prompts derived from relevant documents, a more comprehensive understanding and enrichment of entities within the LLMs can be achieved. One approach to adapt the method would involve leveraging entity linking and disambiguation techniques to connect entities mentioned in the contextual prompts to entries in the external knowledge graph. This linkage would enable a deeper level of insight into these entities by pulling additional structured information directly from the graph. Furthermore, utilizing metadata or relationships present in the external knowledge graph could provide valuable context that complements and enriches the textual context extracted from documents. Additionally, employing hybrid models that combine both contextual prompts and structured data from external knowledge graphs could lead to more robust and accurate representations of entities within LLMs. These hybrid models could leverage both sources of information synergistically to enhance performance across various tasks like question answering, relation extraction, or entity prediction.

Q: What are the potential drawbacks of relying solely on contextual prompts for knowledge infusion in LLMs?

While relying solely on contextual prompts for infusing knowledge into Large Language Models (LLMs) offers several advantages such as efficiency and generalizability, there are also potential drawbacks associated with this approach: Limited Scope: Contextual prompts may not always capture all aspects of an entity's background or related information comprehensively. Depending solely on textual context might result in missing out on nuanced details that could have been available through structured data sources like traditional Knowledge Graphs. Ambiguity: Textual contexts can sometimes be ambiguous or open to interpretation leading to potential errors during inference or predictions. Lack of explicit structure inherent in formalized data sources might introduce noise or incorrect associations when inferring new facts about entities based purely on text-based contexts. Scalability: Relying only on textual contexts for large-scale applications may pose scalability challenges as processing vast amounts of unstructured text data efficiently while maintaining high accuracy levels can be resource-intensive. Quality Control: Ensuring consistency and reliability of inferred facts solely based on textual contexts without validation against established reference points like Knowledge Graphs may raise concerns regarding trustworthiness and quality control mechanisms. Generalization Limitations: The effectiveness of using only contextual prompts may vary across different domains or specialized fields where specific domain expertise is required beyond what is available in natural language texts alone.

Q: How might the use of contextual prompts impact interpretability language models?

The utilization of contextual prompts for infusing knowledge into language models has implications for model interpretability: Enhanced Entity Understanding: Incorporating relevant context alongside input text helps improve model understanding by providing additional background information about entities mentioned within a given task instance. 2 .Explainable Predictions: The inclusion of context allows users to trace back how certain predictions were made by observing which parts influenced decision-making processes within a model. 3 .Contextual Reasoning: By emphasizing surrounding text related to specific entities during fine-tuning stages, it enables better reasoning capabilities within language models when generating responses. 4 .Transparency: Contextual prompt-based approaches offer transparency regarding why certain decisions were reached since they highlight key pieces contributing towards final outcomes. 5 .Error Analysis: With detailed insights provided through enriched contexts during training phases, post-hoc error analysis becomes more informative as it sheds light on areas where improvements need addressing based on misinterpretations stemming from inadequate context representation. These factors collectively contribute towards making language models more interpretable by offering clearer pathways towards understanding their inner workings when tasked with complex NLP assignments involving diverse sets

Core Concepts

The author proposes a method to infuse knowledge into Large Language Models using contextual prompts from domain-specific corpora, eliminating the need for structured knowledge graphs. This approach aims to enhance model performance for downstream tasks efficiently.

Abstract

The content discusses the significance of infusing knowledge into Large Language Models (LLMs) using contextual prompts from domain-specific corpora. By leveraging relevant context from documents, the proposed method aims to improve model performance without relying on structured knowledge graphs. The study compares this approach against other techniques and highlights its advantages in terms of simplicity, scalability, and applicability in low-resource settings. Experimental results demonstrate the effectiveness of using contextual text as discrete prompts for enhancing LLMs across various tasks.

Stats

Hits@1 ↑ 0.798
Hits@5 ↑ 0.801
Hits@10 ↑ 0.804
AED ↓ 2.75
MRR ↑ 0.805

Quotes

"Infusing knowledge directly from documents without having to create knowledge graphs is not only efficient but also more general."
"Our method is extensible to other modalities like tabular data and graphs."
"Our introduction of contextual text as discrete text prompts significantly improves performance across all datasets and metrics."

Key Insights Distilled From

Infusing Knowledge into Large Language Models with Contextual Prompts

by Kinshuk Vasi... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01481.pdf

Infusing Knowledge into Large Language Models with Contextual Prompts

Deeper Inquiries

How can the proposed method be adapted for applications where external knowledge graphs are available?

In scenarios where external knowledge graphs are accessible, the proposed method of using contextual prompts for knowledge infusion in Large Language Models (LLMs) can be enhanced by integrating information from these knowledge graphs. By incorporating data from external sources, such as structured knowledge bases or domain-specific repositories, alongside the contextual prompts derived from relevant documents, a more comprehensive understanding and enrichment of entities within the LLMs can be achieved.
One approach to adapt the method would involve leveraging entity linking and disambiguation techniques to connect entities mentioned in the contextual prompts to entries in the external knowledge graph. This linkage would enable a deeper level of insight into these entities by pulling additional structured information directly from the graph. Furthermore, utilizing metadata or relationships present in the external knowledge graph could provide valuable context that complements and enriches the textual context extracted from documents.
Additionally, employing hybrid models that combine both contextual prompts and structured data from external knowledge graphs could lead to more robust and accurate representations of entities within LLMs. These hybrid models could leverage both sources of information synergistically to enhance performance across various tasks like question answering, relation extraction, or entity prediction.

What are the potential drawbacks of relying solely on contextual prompts for knowledge infusion in LLMs?

While relying solely on contextual prompts for infusing knowledge into Large Language Models (LLMs) offers several advantages such as efficiency and generalizability, there are also potential drawbacks associated with this approach:

Limited Scope: Contextual prompts may not always capture all aspects of an entity's background or related information comprehensively. Depending solely on textual context might result in missing out on nuanced details that could have been available through structured data sources like traditional Knowledge Graphs.

Ambiguity: Textual contexts can sometimes be ambiguous or open to interpretation leading to potential errors during inference or predictions. Lack of explicit structure inherent in formalized data sources might introduce noise or incorrect associations when inferring new facts about entities based purely on text-based contexts.

Scalability: Relying only on textual contexts for large-scale applications may pose scalability challenges as processing vast amounts of unstructured text data efficiently while maintaining high accuracy levels can be resource-intensive.

Quality Control: Ensuring consistency and reliability of inferred facts solely based on textual contexts without validation against established reference points like Knowledge Graphs may raise concerns regarding trustworthiness and quality control mechanisms.

Generalization Limitations: The effectiveness of using only contextual prompts may vary across different domains or specialized fields where specific domain expertise is required beyond what is available in natural language texts alone.

How might the use of contextual prompts impact interpretability language models?

The utilization of contextual prompts for infusing knowledge into language models has implications for model interpretability:

Enhanced Entity Understanding: Incorporating relevant context alongside input text helps improve model understanding by providing additional background information about entities mentioned within a given task instance.

2 .Explainable Predictions: The inclusion of context allows users to trace back how certain predictions were made by observing which parts influenced decision-making processes within a model.
3 .Contextual Reasoning: By emphasizing surrounding text related to specific entities during fine-tuning stages, it enables better reasoning capabilities within language models when generating responses.
4 .Transparency: Contextual prompt-based approaches offer transparency regarding why certain decisions were reached since they highlight key pieces contributing towards final outcomes.
5 .Error Analysis: With detailed insights provided through enriched contexts during training phases, post-hoc error analysis becomes more informative as it sheds light on areas where improvements need addressing based on misinterpretations stemming from inadequate context representation.
These factors collectively contribute towards making language models more interpretable by offering clearer pathways towards understanding their inner workings when tasked with complex NLP assignments involving diverse sets

Infusing Knowledge into Large Language Models with Contextual Prompts: A Detailed Study