Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning
Summary: arXiv:2604.05396v1 Announce Type: new
Abstract
Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. We posit that different domains may share underlying reasoning structures, enabling source-domain demonstrations to improve target-domain inference despite semantic mismatch.
Introduction
In recent years, in-context learning (ICL) has gained attention as a promising approach in the field of artificial intelligence. However, its reliance on expert demonstrations from the same domain poses challenges, particularly in areas where such annotations are limited. Our research aims to explore the potential of leveraging cross-domain knowledge transfer to enhance ICL capabilities.
Research Objectives
The primary objectives of our study include:
- To investigate the feasibility of cross-domain knowledge transfer in the context of ICL.
- To identify effective retrieval methods that facilitate this transfer.
- To understand the underlying mechanisms that contribute to improved inference in target domains.
Methodology
Our empirical study involved a comprehensive analysis of various retrieval methods to assess their effectiveness in achieving cross-domain knowledge transfer. We conducted experiments across multiple domains, focusing on the following key aspects:
- Selection of source-domain demonstrations.
- Evaluation of target-domain inference outcomes.
- Identification of an absorption threshold for positive transfer.
Findings
Our findings reveal that cross-domain ICL can indeed facilitate conditional positive transfer. Notably, we discovered a clear absorption threshold; when the number of retrieved examples surpasses this threshold, the likelihood of positive transfer increases significantly. Additionally, our analysis indicates that the gains observed in performance are primarily due to the repair of reasoning structures by the retrieved cross-domain examples, rather than relying solely on semantic cues.
Implications
The implications of our study are profound. By validating the potential of cross-domain knowledge transfer, we motivate the community to explore more effective retrieval approaches in ICL. This research opens up new avenues for enhancing AI capabilities, particularly in scenarios where domain-specific expert knowledge is lacking.
Conclusion
In conclusion, our empirical study substantiates the feasibility of leveraging cross-domain knowledge transfer to enhance in-context learning performance. As AI continues to evolve, understanding and implementing effective cross-domain strategies will be crucial in addressing the challenges posed by limited expert annotations.
For those interested, our implementation is available at GitHub Repository.