Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models

Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

Abstract
Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking. When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos or external retrievers might fail. To bridge the gap between limited demos and OOD queries, we propose Self-Demos, a novel prompting method that elicits the inherent generalizability in LLMs by query-aware demo generation. The generated demos strategically interpolate between existing demos and the given query, transforming the query from OOD to ID. To evaluate the effectiveness of our approach, we manually constructed OOD-Toolset, a dataset in the tool-using scenario with over 300 real-world APIs and 1000 instances, each consisting of three tool-use cases as demos and an OOD query. Thorough experiments on our dataset and two public math benchmarks have shown that our method can outperform state-of-the-art baselines in the OOD setting. Moreover, we conduct a range of analyses to validate Self-Demos’s generalization and provide more insights.
Anthology ID:
2024.findings-naacl.243
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3829–3845
Language:
URL:
https://aclanthology.org/2024.findings-naacl.243
DOI:
10.18653/v1/2024.findings-naacl.243
Bibkey:
Cite (ACL):
Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024. Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3829–3845, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (He et al., Findings 2024)
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https://preview.aclanthology.org/ingest-2024-clasp/2024.findings-naacl.243.pdf
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