arXiv:2601.16946v2 Announce Type: replace Abstract: 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 limitat
The rapid adoption of large language models for various text analysis tasks necessitates robust and consistent methodologies for fine-grained operations like span labeling, addressing current ad-hoc solutions.
Improving span labeling techniques is critical for enhancing the precision and reliability of LLMs in applications such as named entity recognition and error detection, thereby expanding their utility and trustworthiness.
This research moves towards standardizing and optimizing how generative LLMs interact with specific parts of input text, potentially leading to more consistent and effective downstream applications.
- · AI developers
- · NLP researchers
- · Industries dependent on text analysis
- · Developers relying on inconsistent ad-hoc LLM prompting
More accurate and efficient information extraction and text processing using LLMs.
Accelerated development of AI agents capable of understanding and manipulating text with higher precision.
Potential for new AI-driven applications requiring granular textual understanding across various sectors.
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Read at arXiv cs.CL