提出新解码方法，让大模型更准地标注文本片段。
Strategies for Span Labeling with Large Language Models
- 将标注策略分为三类：标记文本、编号位置、匹配内容
- 新方法LogitMatch提升内容匹配准确率，部分任务超越其他方法
- 适合需要精准文本片段标注的NLP应用
大语言模型在命名实体识别等文本分析任务中应用日益广泛。与编码器模型不同，生成式架构缺乏显式引用输入特定部分的机制，导致各种临时提示策略效果不一。本文将这些策略归为三类：标记输入文本、索引片段数值位置、匹配片段内容。针对内容匹配的不足，提出LogitMatch——一种强制输出对齐有效输入片段的约束解码方法。在四个不同任务上评估所有方法，发现虽然标记仍是稳健基线，但LogitMatch通过消除匹配问题，在部分设置下优于其他策略。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-based models, however, generative architectures lack an explicit mechanism to refer to specific parts of their input. This leads to a variety of ad-hoc prompting strategies for span labeling, often with inconsistent results. In this paper, we categorize these strategies into three families: tagging the input text, indexing numerical positions of spans, and matching span content. To address the limitations of content matching, we introduce LogitMatch, a new constrained decoding method that forces the model's output to align with valid input spans. We evaluate all methods across four diverse tasks. We find that while tagging remains a robust baseline, LogitMatch improves upon competitive matching-based methods by eliminating span matching issues and outperforms other strategies in some setups.
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