Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910107513651200 |
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| author | Liu, Le Li, Zhiming Yan, Jianzhi Yuan, Zike Chen, Shiwei Pan, Youcheng Tang, Buzhou Chen, Qingcai Xiang, Yang Sun, Danny Dongning |
| author_facet | Liu, Le Li, Zhiming Yan, Jianzhi Yuan, Zike Chen, Shiwei Pan, Youcheng Tang, Buzhou Chen, Qingcai Xiang, Yang Sun, Danny Dongning |
| contents | 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. To test this hypothesis, we conduct a comprehensive empirical study of different retrieval methods to validate the feasibility of achieving cross-domain knowledge transfer under the in-context learning setting. Our results demonstrate conditional positive transfer in cross-domain ICL. We identify a clear example absorption threshold: beyond it, positive transfer becomes more likely, and additional demonstrations yield larger gains. Further analysis suggests that these gains stem from reasoning structure repair by retrieved cross-domain examples, rather than semantic cues. Overall, our study validates the feasibility of leveraging cross-domain knowledge transfer to improve cross-domain ICL performance, motivating the community to explore designing more effective retrieval approaches for this novel direction.\footnote{Our implementation is available at https://github.com/littlelaska/ICL-TF4LR} |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_05396 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning Liu, Le Li, Zhiming Yan, Jianzhi Yuan, Zike Chen, Shiwei Pan, Youcheng Tang, Buzhou Chen, Qingcai Xiang, Yang Sun, Danny Dongning Artificial Intelligence 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. To test this hypothesis, we conduct a comprehensive empirical study of different retrieval methods to validate the feasibility of achieving cross-domain knowledge transfer under the in-context learning setting. Our results demonstrate conditional positive transfer in cross-domain ICL. We identify a clear example absorption threshold: beyond it, positive transfer becomes more likely, and additional demonstrations yield larger gains. Further analysis suggests that these gains stem from reasoning structure repair by retrieved cross-domain examples, rather than semantic cues. Overall, our study validates the feasibility of leveraging cross-domain knowledge transfer to improve cross-domain ICL performance, motivating the community to explore designing more effective retrieval approaches for this novel direction.\footnote{Our implementation is available at https://github.com/littlelaska/ICL-TF4LR} |
| title | Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.05396 |