CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation

ArXi:2604.19488v1 Announce Type: new Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-context learning provides a viable solution that boosts the model's performance via prompting its input with expert-curated, in-domain exemplars. However, in many real-world, expertise-scarce domains, such as low-resource scientific disciplines, emerging biomedical subfields, or niche legal jurisdictions, such high-quality in-domain nstrations are inherently limited or entirely unavailable, thereby cons