BT-APE automates prompt engineering for requirements classification using backtracking search and dynamic examples, matching PE2 accuracy while using 72% fewer tokens and 66% less time than that baseline.
One prompt is not enough: Automated construction of a mixture-of-expert prompts
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes compiling preference pairs into readable natural-language specifications for inference-time LLM alignment, claiming outperformance over DPO on dense-preference domains.
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.
citing papers explorer
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BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
BT-APE automates prompt engineering for requirements classification using backtracking search and dynamic examples, matching PE2 accuracy while using 72% fewer tokens and 66% less time than that baseline.
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Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Proposes compiling preference pairs into readable natural-language specifications for inference-time LLM alignment, claiming outperformance over DPO on dense-preference domains.
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TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness gain on 11 datasets.