Papers with adaptive filter-then-rerank paradigm

1 papers
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (2023.findings-emnlp)

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Challenge: Large Language Models have made remarkable strides in various tasks, but whether they are competitive few-shot solvers remains an open question.
Approach: They propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs.
Outcome: The proposed system achieves promising improvements on various IE tasks with acceptable time and cost investment.

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