Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
This addresses the challenge of boosting LLM reasoning in specialized domains where expert-crafted examples are scarce, though it appears incremental as it builds on existing retrieval and in-context learning techniques.
The paper tackles the problem of limited applicability of in-context learning in expertise-scarce domains by leveraging cross-domain demonstrations, achieving an average improvement of 1.8 over state-of-the-art methods in mathematical and logical reasoning tasks.
Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human-level performance. Current boosting strategies rely on expert-crafted in-domain demonstrations, limiting their applicability in expertise-scarce domains, such as specialized mathematical reasoning, formal logic, or legal analysis. In this work, we demonstrate the feasibility of leveraging cross-domain demonstrating examples to boost the LLMs' reasoning performance. Despite substantial domain differences, many reusable implicit logical structures are shared across domains. In order to effectively retrieve cross-domain examples for unseen domains under investigation, in this work, we further propose an effective retrieval method, called domain-invariant neurons-based retrieval (\textbf{DIN-Retrieval}). Concisely, DIN-Retrieval first summarizes a hidden representation that is universal across different domains. Then, during the inference stage, we use the DIN vector to retrieve structurally compatible cross-domain demonstrations for the in-context learning. Experimental results in multiple settings for the transfer of mathematical and logical reasoning demonstrate that our method achieves an average improvement of 1.8 over the state-of-the-art methods \footnote{Our implementation is available at https://github.com/Leon221220/DIN-Retrieval}.