Introduction
Reference resolution is a crucial aspect of understanding and navigating complex contexts. From previous turns in a conversation to non-conversational entities on a user’s screen, context is essential for effective communication. While Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, their application in reference resolution, particularly for non-conversational entities, remains underutilized. This article explores the potential of LLMs in resolving references of various types, and how they can be leveraged to create an effective system.
Reference Resolution: A Critical Problem
Reference resolution is an important problem, one that is essential to understand and successfully handle contexts of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user’s screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized.
Converting Reference Resolution into a Language Modeling Problem
This paper demonstrates how LLMs can be used to create an effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality.
Experimental Results
We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.
Conclusion
In conclusion, this paper has demonstrated the potential of LLMs in resolving references of various types, and how they can be leveraged to create an effective system. The results show significant improvements over existing systems, and comparable performance to state-of-the-art models. This has important implications for the development of more effective and efficient reference resolution systems.
Frequently Asked Questions
Q: What is reference resolution?
Reference resolution is the process of identifying and understanding the meaning of references to entities, concepts, or objects in a given context. This can include resolving references to previous turns in a conversation, or identifying entities on a user’s screen or running in the background.
Q: What is the significance of reference resolution?
Reference resolution is critical for effective communication and understanding. It allows systems to accurately identify and understand the meaning of references, which is essential for tasks such as question answering, information retrieval, and natural language processing.
Q: How do LLMs improve reference resolution?
LLMs can improve reference resolution by leveraging their ability to generate and manipulate text to identify and understand the meaning of references. This can include generating text that is similar to the reference, or manipulating the reference to make it more understandable.
Q: What are the limitations of LLMs in reference resolution?
While LLMs have shown significant potential in reference resolution, they are not without limitations. For example, they may struggle with references that are ambiguous or unclear, or with references that require a deep understanding of the context.
Q: What are the implications of this research?
This research has important implications for the development of more effective and efficient reference resolution systems. It suggests that LLMs can be a powerful tool for resolving references, and can be used to improve the accuracy and efficiency of reference resolution systems.
