Prompt-Based Anaphora Resolution in Large Language Models

Abstract

With the advancement of Large Language Models (LLMs), the scope of research in the Natural Language Processing (NLP) domain has significantly shifted. The LLM has context-based advanced language understanding that is suitable for various types of discourse analysis. Creating suitable prompts can effectively guide the model’s responses toward the desired outcome. Anaphora resolution is a complex problem that is highly context-dependent. This paper attempted to explore a prompt-based LLM technique for the resolution of anaphora. Our experiment used a \textbf{text-based question} prompt within the OpenAI LLM framework. The experiment is conducted in the Assamese language, initially using a rule-based system. The results are then compared with those obtained from a prompt-based approach. The main contribution of this paper is the exploration of prompt engineering techniques for anaphora resolution. The results indicate that the prompt-based approach is significantly superior to the rule-based approach.