LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation

Abstract:Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification. While Large Language Models (LLMs) show promise, a significant gap remains between the theoretical capability of LLMs and their practical utility for researchers. This paper introduces LinguistAgent, an integrated, user-friendly platform that leverages a reflective multi-model architecture to automate linguistic annotation. The platform comprises an Annotator and an optional Reviewer to simulate a peer-review process. This platform supports comparative experiments across three main paradigms: Prompt Engineering (Zero-shot/Few-shot/Chain-of-thought), Retrieval-Augmented Generation, and Fine-tuning. We demonstrate LinguistAgent's efficacy by replicating the task of metaphor identification from a published study, which provides real-time token-level evaluation (F1 and Cohen's kappa) against human gold standards. The application and codes are released on this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2602.05493 [cs.CL]

(or arXiv:2602.05493v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2602.05493 Focus to learn more arXiv-issued DOI via DataCite

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