Error slice discovery associates structured patterns with model errors. Existing methods discover error slices by clustering the error-prone samples with similar patterns or assigning discrete attributes to each sample for post-hoc analysis. While these methods aim for interpretability and easier mitigation through reweighting or rebalancing, they may not capture the full complexity of error patterns due to incomplete or missing attributes. Contrary to the existing approach, this paper utilizes the reasoning capabilities of the Large Language Model (LLM) to analyze complex error patterns and generate testable hypotheses. This paper proposes LADDER: Language Driven slice Discovery and Error Rectification. It first projects the model's representation into a language-aligned feature space (eg CLIP) to preserve semantics in the original model feature space. This ensures the accurate retrieval of sentences that highlight the model's errors. Next, the LLM utilizes the sentences and generates hypotheses to discover error slices. Finally, we mitigate the error by fine-tuning the classification head by creating a group-balanced dataset using the hypotheses. Our entire method does not require any attribute annotation, either explicitly or through external tagging models. We validate our method with \textbf{five} image classification datasets.

翻译：错误切片发现将结构化模式与模型错误相关联。现有方法通过聚类具有相似模式的易错样本或为每个样本分配离散属性进行事后分析来发现错误切片。尽管这些方法旨在通过重新加权或重新平衡实现可解释性和更易缓解，但由于属性不完整或缺失，它们可能无法捕捉错误模式的全部复杂性。与现有方法不同，本文利用大语言模型（LLM）的推理能力来分析复杂错误模式并生成可检验的假设。本文提出LADDER：语言驱动的切片发现与错误校正。该方法首先将模型表示投影到语言对齐的特征空间（如CLIP）中，以保留原始模型特征空间中的语义。这确保了能够准确检索突出模型错误的句子。接着，LLM利用这些句子生成假设以发现错误切片。最后，我们通过使用假设创建组平衡数据集来微调分类头，从而缓解错误。我们的整个方法不需要任何属性标注，无论是显式标注还是通过外部标记模型。我们在\textbf{五个}图像分类数据集上验证了我们的方法。