Investigating the Impact of Prompt Syntax and Supplementary Information on Knowledge Retrieval from Pretrained Language Models

Core Concepts

Prompt syntax and supplementary information have a significant impact on the knowledge retrieval performance of pretrained language models. Clausal syntax prompts outperform appositive syntax prompts, and range information is more helpful than domain information for improving retrieval performance.

Abstract

The paper investigates the impact of prompt syntax and supplementary information on the knowledge retrieval performance of pretrained language models (PLMs). The authors introduce CONPARE-LAMA, a controlled paraphrasing probe that enables the systematic study of these factors.

Key highlights:

• Clausal syntax prompts (compound, complex) outperform appositive syntax prompts across different PLMs and datasets. Clausal prompts lead to more consistent knowledge retrieval and lower response uncertainty.
• Adding range information (e.g., "Paris is the capital of [MASK], which is a country") boosts performance more than adding domain information (e.g., "Paris is a city and is the capital of [MASK]"), though domain information is more reliably helpful across syntactic forms.
• The authors find that information helpful in isolation can be detrimental when combined, suggesting that PLMs struggle to efficiently incorporate supplementary information, especially when presented in appositive syntax.
• Consistency of knowledge retrieval is higher for clausal prompts compared to appositive prompts, indicating that syntax plays a crucial role in how PLMs process and retrieve relational knowledge.

The findings provide insights into the fragility of information flow in language representations achieved by PLMs and suggest that specialized training approaches leveraging controlled prompt engineering could improve their knowledge retrieval capabilities.

Stats

Paris is a city and is the capital of [MASK].
Paris is the capital of [MASK], which is a country.
The native language of [S] is [MASK].
[S] natively speaks [MASK].
[S] can be described as [MASK].

Quotes

"Clausal syntax prompts (compound, complex) outperform appositive syntax prompts across different PLMs and datasets."
"Adding range information (e.g., "Paris is the capital of [MASK], which is a country") boosts performance more than adding domain information (e.g., "Paris is a city and is the capital of [MASK]"), though domain information is more reliably helpful across syntactic forms."
"The authors find that information helpful in isolation can be detrimental when combined, suggesting that PLMs struggle to efficiently incorporate supplementary information, especially when presented in appositive syntax."

Key Insights Distilled From

Dissecting Paraphrases

by Stephan Linz... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01992.pdf

Deeper Inquiries

How can the findings from this study be leveraged to improve the knowledge representation and reasoning capabilities of pretrained language models

The findings from this study can be instrumental in enhancing the knowledge representation and reasoning capabilities of pretrained language models. By understanding the impact of prompt syntax and supplementary information on knowledge retrieval performance, researchers and developers can tailor prompts more effectively to elicit accurate responses from the models. Leveraging the insights gained, one approach could involve incorporating syntax-aware pre-training paradigms. By training models on data that intentionally includes diverse syntactic structures and carefully crafted prompts, the models can learn to better understand and reason with the information provided. This specialized training can help the models encode more robust knowledge representations and improve their ability to retrieve and reason with complex information.

What other types of supplementary information, beyond domain and range constraints, could be explored to further enhance knowledge retrieval performance

Beyond domain and range constraints, exploring additional types of supplementary information can further enhance knowledge retrieval performance. Some potential avenues to explore include temporal constraints, causal relationships, spatial information, and contextual constraints. Temporal constraints can provide information about the timing or sequence of events, allowing models to understand the chronological order of historical events or the evolution of concepts over time. Causal relationships can help models infer cause-and-effect connections between entities or events, enabling them to reason about the consequences of certain actions. Spatial information can offer insights into the physical locations or spatial relationships between entities, enhancing the models' understanding of geographical contexts. Contextual constraints can provide situational information that guides the interpretation of the knowledge, helping the models make more informed decisions based on the context in which the information is presented.

How do the observed patterns of knowledge retrieval consistency and response uncertainty vary across different domains and tasks beyond relational knowledge extraction

The observed patterns of knowledge retrieval consistency and response uncertainty can vary across different domains and tasks, beyond relational knowledge extraction. In domains where the information is more structured and well-defined, such as scientific or technical domains, we may see higher knowledge retrieval consistency as the data is more standardized and less ambiguous. On the other hand, in domains with more subjective or nuanced information, such as social sciences or humanities, we may observe lower knowledge retrieval consistency due to the interpretive nature of the data. Response uncertainty may also vary based on the complexity of the task - simpler tasks may result in lower uncertainty as the models can more confidently predict the correct answers, while more complex tasks may lead to higher uncertainty as the models grapple with multiple potential interpretations or outcomes. Additionally, the nature of the data and the specific requirements of the task can influence the patterns of knowledge retrieval consistency and response uncertainty, highlighting the importance of domain-specific considerations in evaluating and improving the performance of pretrained language models.