Papers with COPLE

1 papers
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement (2024.emnlp-main)

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Challenge: Large language models (LLMs) demonstrate exceptional instruct-following ability to complete downstream tasks.
Approach: They propose a black-box combinatorial optimization framework that iteratively improves lexical choices in prompts by a search strategy related to word influence.
Outcome: The proposed framework recovers the model's ability to instruct-follow and solve downstream tasks even when the variations are imperceptible to humans.

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