arxiv preprint - ReALM: Reference Resolution As Language Modeling

In this episode, we discuss ReALM: Reference Resolution As Language Modeling by Joel Ruben Antony Moniz, Soundarya Krishnan, Melis Ozyildirim, Prathamesh Saraf, Halim Cagri Ates, Yuan Zhang, Hong Yu, Nidhi Rajshree. This paper presents a method for using Large Language Models (LLMs) to resolve references, including complex ones such as entities on a user's screen or in the background, by framing reference resolution as a language modeling task. The proposed system shows significant improvements, with over 5% gains in handling on-screen references, compared to an existing system. Moreover, the paper reports that even the smallest model within their framework performs comparably to GPT-4, while their larger models outperform GPT-4.