Strategies for Span Labeling with Large Language Models
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Recent research reveals that large language models (LLMs) face challenges in text analysis tasks like named entity recognition due to a lack of mechanisms for referencing input segments. The study categorizes prompting strategies into three types and introduces LogitMatch, a new method that aligns model outputs with valid input spans. Evaluations demonstrate that while traditional tagging is effective, LogitMatch enhances performance by resolving span matching issues, outperforming other strategies in several scenarios. This advancement could refine how LLMs are applied in text analysis, leading to more accurate outcomes.
New Strategies for Span Labeling with Large Language Models
Recent research highlights the evolving role of large language models (LLMs) in text analysis, particularly for tasks such as named entity recognition. A notable challenge with generative architectures is their lack of an explicit mechanism for referencing specific input segments, leading to inconsistent prompting strategies for span labeling.
Introduction of LogitMatch
Researchers have introduced LogitMatch, a novel constrained decoding method designed to enhance the alignment between model output and valid input spans. This method offers a more structured approach to span labeling, addressing previous issues with matching-based techniques.
Evaluation Across Diverse Tasks
The study evaluates these methods across four tasks. Findings indicate that while tagging remains a strong baseline, LogitMatch demonstrates superior performance in certain setups. By effectively eliminating issues related to span matching, LogitMatch outstrips other strategies in various scenarios.
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📰 Original Source: https://arxiv.org/abs/2601.16946v1
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