In-context learning (ICL) relies heavily on high-quality demonstrations drawn from large annotated corpora. Existing approaches detect noisy annotations by ranking local perplexities, presuming that noisy samples yield higher perplexities than their clean counterparts. However, this assumption breaks down when the noise ratio is high and many demonstrations are flawed.We re-examine the perplexity-based paradigm for text generation under noisy annotations, highlighting two sources of bias in perplexity: the annotation itself and the domain-specific knowledge inherent in large language models (LLMs). To overcome these biases, we introduce a dual-debiasing framework that uses synthesized neighbors to explicitly correct perplexity estimates, yielding a robust Sample Cleanliness Score. This metric uncovers absolute sample cleanliness regardless of the overall corpus noise level.Extensive experiments demonstrate our method’s superior noise-detection capabilities and show that its final ICL performance is comparable to that of a fully clean demonstration corpus. Moreover, our approach remains robust even when noise ratios are extremely high.

Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers. Despite the versatility of encoder-decoder models in numerous NLP tasks, the structured pruning methods on such models are relatively less explored compared to encoder-only models. In this study, we investigate the behavior of the structured pruning of the encoder-decoder models in the decoupled pruning perspective of the encoder and decoder component, respectively. Our findings highlight two insights: (1) the number of decoder layers is the dominant factor of inference speed, and (2) low sparsity in the pruned encoder network enhances generation quality. Motivated by these findings, we propose a simple and effective framework, NASH, that narrows the encoder and shortens the decoder networks of encoder-decoder models. Extensive experiments on diverse generation and inference tasks validate the effectiveness of our method in both speedup and output quality.