We conduct the first empirical study on using knowledge transfer to improve the generalization ability of large language models (LLMs) in software engineering tasks, which often require LLMs to generalize beyond their training data. Our proposed general knowledge transfer approach guides the LLM towards a similar and familiar API or code snippet it has encountered before, improving the model's generalization ability for unseen knowledge. We apply this approach to three software engineering tasks: API inference, code example generation, and FQN inference, and find transfer span, transfer strategy, and transfer architecture as key factors affecting the method. Our findings demonstrate the feasibility of knowledge transfer and its potential to enhance LLMs' performance in various software engineering tasks. The effectiveness of knowledge transfer varies depending on the target domain and task, with the hierarchical strategy being more effective than direct transfer, and AI-Chain outperforming CoT in prompt design. The implications of these findings extend beyond software engineering tasks and suggest that knowledge transfer can enhance LLMs' ability to handle unknowns in any natural language task.

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