ExpeTrans: LLMs Are Experiential Transfer Learners
This addresses the impracticality of manually collecting experiences for diverse LLM tasks, offering a cost-effective generalization method.
The paper tackles the problem of high human labor or time costs for gathering textual task-solving experiences for LLMs by proposing an autonomous experience transfer framework, which improves LLM performance on 13 datasets.
Recent studies provide large language models (LLMs) with textual task-solving experiences via prompts to improve their performance. However, previous methods rely on substantial human labor or time to gather such experiences for each task, which is impractical given the growing variety of task types in user queries to LLMs. To address this issue, we design an autonomous experience transfer framework to explore whether LLMs can mimic human cognitive intelligence to autonomously transfer experience from existing source tasks to newly encountered target tasks. This not only allows the acquisition of experience without extensive costs of previous methods, but also offers a novel path for the generalization of LLMs. Experimental results on 13 datasets demonstrate that our framework effectively improves the performance of LLMs. Furthermore, we provide a detailed analysis of each module in the framework.