LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation
Bingru Li
Why It Matters
What makes this one worth your time
This research addresses a critical bottleneck in humanities and social sciences by enhancing the efficiency of linguistic annotation, which is essential for various research applications.
LinguistAgent automates linguistic annotation with a user-friendly multi-model platform.
Summary
The paper presents LinguistAgent, a platform designed to automate linguistic annotation tasks, particularly metaphor identification, using a reflective multi-model architecture that includes an Annotator and Reviewer for peer-review simulation.
Key contributions
- Introduction of LinguistAgent as a multi-model platform for automated linguistic annotation.
- Implementation of real-time token-level evaluation metrics (F1 and Cohen's kappa) against human standards.
- Support for various paradigms in linguistic annotation, including Prompt Engineering and Retrieval-Augmented Generation.
Notable insights
- The integration of a Reviewer component to simulate peer review is a novel approach to enhance the reliability of automated annotations.
- The platform's comparative experiments across different paradigms provide a comprehensive evaluation of LLM capabilities in linguistic tasks.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2602.05493v2 Announce Type: replace-cross 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 https://github.com/Bingru-Li/LinguistAgent.