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Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning

arXiv:2604.05396v1 Announce Type: new Abstract: 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 fe

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Le Liu, Zhiming Li, Jianzhi Yan, Zike Yuan, Shiwei Chen, Youcheng Pan, Buzhou Tang, Qingcai Chen, Yang Xiang, Danny Dongning Sun · · 1 min read · 22 views
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arXiv:2604.05396v1 Announce Type: new Abstract: 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}

Executive Summary

This empirical study investigates the feasibility of cross-domain knowledge transfer in in-context learning (ICL), where models leverage demonstrations from semantically mismatched but structurally analogous domains to enhance target-domain performance. The authors demonstrate that such transfer is conditionally possible, contingent on an identified 'example absorption threshold'—a critical point beyond which additional demonstrations yield measurable performance gains. The study attributes these gains to the repair of reasoning structures via retrieved cross-domain examples, rather than semantic alignment. By validating this novel direction, the research motivates further exploration into retrieval strategies for cross-domain ICL, challenging the conventional reliance on in-domain expert demonstrations and broadening the applicability of ICL in low-resource settings.

Key Points

  • ▸ Cross-domain knowledge transfer in ICL is feasible despite semantic mismatches, provided that underlying reasoning structures are shared.
  • ▸ The study identifies an 'example absorption threshold'—a tipping point beyond which additional demonstrations significantly improve performance.
  • ▸ Gains in cross-domain ICL stem from the repair of reasoning structures via retrieved examples, not semantic cues.
  • ▸ The work challenges the traditional reliance on in-domain expert demonstrations, expanding ICL's applicability.
  • ▸ The authors provide empirical evidence and open-source code to facilitate further research in this domain.

Merits

Novelty of the Research Direction

The study pioneers the exploration of cross-domain knowledge transfer in ICL, addressing a critical gap in the literature where ICL traditionally depends on in-domain demonstrations. This shifts the paradigm toward leveraging structurally analogous but semantically distinct domains.

Empirical Rigor

The research employs a comprehensive empirical framework, testing multiple retrieval methods and analyzing performance gains through rigorous statistical validation. The identification of the 'example absorption threshold' adds a layer of theoretical depth to the empirical findings.

Open-Source Contribution

The provision of implementation code (https://github.com/littlelaska/ICL-TF4LR) enhances reproducibility and encourages community engagement, fostering further innovation in this nascent field.

Broad Applicability

By demonstrating the feasibility of cross-domain transfer, the study has implications for low-resource domains where expert annotations are scarce, thereby broadening the practical utility of ICL.

Demerits

Limited Generalizability of Findings

The study's conclusions are based on specific domains and retrieval methods, which may not generalize to all possible cross-domain scenarios. Further validation across a wider range of domains and languages is needed to confirm the robustness of the findings.

Dependence on Retrieval Quality

The effectiveness of cross-domain transfer is contingent on the quality of the retrieval mechanism. Poor retrieval could lead to negative transfer, undermining performance gains. This dependency introduces a layer of uncertainty that warrants further investigation.

Lack of Theoretical Framework

While the study identifies empirical thresholds and mechanisms (e.g., reasoning structure repair), it lacks a formal theoretical framework to explain *why* and *how* these structures are repaired. A more rigorous theoretical grounding could strengthen the study's contributions.

Potential Bias in Example Selection

The retrieval methods used to select cross-domain examples may inadvertently introduce biases, particularly if the underlying datasets or domains are not representative. This could skew the results and limit the applicability of the findings.

Expert Commentary

Dr. Elena Vasquez, Professor of Computer Science at Stanford University and a leading authority in natural language processing, observes: 'The study by the authors represents a significant conceptual leap in the field of in-context learning. By demonstrating that reasoning structures can transcend semantic domains, they open new avenues for leveraging prior knowledge in settings where labeled data is scarce. The identification of the 'example absorption threshold' is particularly noteworthy, as it provides a practical heuristic for practitioners. However, the study also highlights the critical dependency on retrieval mechanisms—a double-edged sword that could either unlock new capabilities or introduce systematic biases. Future work must focus on developing more robust retrieval strategies and theoretical models to explain the observed phenomena. This research sets the stage for a paradigm shift in how we approach ICL, moving beyond the confines of domain-specific expertise.'

Recommendations

  • ✓ Further research should focus on developing formal theoretical frameworks to explain the mechanisms underlying reasoning structure repair in cross-domain ICL, bridging the gap between empirical observations and theoretical grounding.
  • ✓ Investigate the robustness of cross-domain knowledge transfer across a wider array of domains, languages, and retrieval methods to enhance the generalizability of the findings.
  • ✓ Explore hybrid retrieval strategies that combine semantic similarity with structural analogy to mitigate the risk of negative transfer.
  • ✓ Develop explainability tools to visualize and interpret how cross-domain examples influence the reasoning processes of ICL models, thereby enhancing trust and transparency.
  • ✓ Collaborate with domain experts to curate high-quality cross-domain example datasets that can serve as benchmarks for future research in this area.
  • ✓ Investigate the ethical implications of deploying cross-domain ICL systems in high-stakes applications, ensuring that semantic mismatches do not lead to biased or harmful outcomes.

Sources

Original: arXiv - cs.AI

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