1. Introduction

The Large Language Model (LLM) is based on a converter architecture and has billions of parameters, demonstrating excellent natural language processing capabilities. LLMs have been applied in various fields such as education, medicine, and finance, especially in the field of education. They assist teachers in resource acquisition, text and image generation, and achieve personalized teaching and evaluation. LLMs can also provide students with personalized learning materials to help improve their language skills. In the field of language assessment, LLMs have shown potential for application, but also face practical challenges.

2. The application prospects of big language models in language assessment

The application prospects of Large Language Models (LLMs) in language assessment are broad, involving three major stages: assessment development, implementation, scoring, and feedback. In the evaluation and development process, LLMs can improve proposition efficiency, reduce costs, generate multiple question types, predict question difficulty, and reduce the testing process. The customizability of LLMs enables them to generate test questions that comply with proposition rules. The AI digital human video generation platform improves the quality and efficiency of digital human video creation, enhancing the authenticity, fun, and interactivity of assessments. In the implementation of the assessment, LLMs serve as intelligent language companions in oral exams, improving exam efficiency and fairness, and the scoring reliability of the human-machine dialogue mode is higher. LLMs empowered SDS can simulate more natural communication scenarios and reflect individuals' performance in complex interactions. In terms of grading and feedback, LLMs provide multi-dimensional automatic grading and feedback in writing and speaking assessments, improving grading efficiency. The application of LLMs in formative assessment has also shown potential, providing detailed writing feedback without affecting learning outcomes, helping to improve educational quality and promote educational equity.

3. The practical challenges of applying big language models to language assessment

The application of Large Language Models (LLMs) in language assessment faces practical challenges, including the "illusion" problem of generating inaccurate or misleading content, the interpretability of models, the problem of generalization of data and models, and ethical issues. LLMs may produce outputs that are inconsistent with reality, especially on sensitive topics where serious consequences may arise. Its lack of interpretability makes it difficult to locate errors, affecting the transparency and trustworthiness of evaluation results. The quantity and quality of training data affect the generalization ability of the model, which may lead to inaccurate scoring and fairness issues. Ethical issues involve data and algorithm biases, digital divide, and privacy protection, which may affect the fairness of assessment results, exacerbate educational inequality, and existing audit procedures have failed to effectively address governance issues brought about by LLMs.

4. Conclusion

Summarized the role and value of Large Language Models (LLMs) in language assessment, and emphasized that their application in the field of language assessment has become a trend. Faced with new opportunities and challenges, professionals should actively embrace intelligent technology, master technology, and embrace the new era. On a theoretical level, this paper explores the opportunities and challenges of empowering language assessment with LLMs based on the framework of "assessment use argumentation", comprehensively discusses validity issues, and provides theoretical basis for application. On a practical level, it is necessary for the government, universities, language assessment institutions, and other organizations to work together to carry out teacher training and reduce the risk of abuse and misuse; Establish a collaborative platform, accumulate data, and achieve data sharing; Develop ethical standards, enhance ethical and moral awareness, improve transparency, and ensure educational equity. At the same time, researchers and practitioners in the field of language assessment should have in-depth exchanges and close cooperation with experts and scholars in the field of artificial intelligence, leverage their respective strengths, and achieve win-win cooperation.