ClusterFewshot: Improving Few-shot Optimization for LLMs workflow
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Computer Science > Computation and Language
arXiv:2609.25939 (cs)
[Submitted on 22 Sep 2026]
Title:ClusterFewshot: Improving Few-shot Optimization for LLMs workflow
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Abstract:The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on new tasks. Recent methods improve this process by augmenting prompts with successful reasoning paths. However, their demonstration selection relies on random sampling or metric-based rankings, overlooking the semantic structure of the task. We propose ClusterFewshot, a strategy that combines semantic structuring with utility-aware scoring to construct representative and effective few-shot demonstration sets. Evaluated within DSPy-based pipelines, ClusterFewshot substantially reduces optimization cost across multiple benchmarks, while consistently improving accuracy relative to prior bootstrap-based methods in both standalone prompt tuning and hybrid prompt-weight optimization.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.25939 [cs.CL] |
| (or arXiv:2609.25939v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.25939
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ClusterFewshot: Improving Few-shot Optimization for LLMs workflow, by Omri Bar Haim and 2 other authors
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