Summary of Realm: Reference Resolution As Language Modeling, by Joel Ruben Antony Moniz et al.
ReALM: Reference Resolution As Language Modeling
by Joel Ruben Antony Moniz, Soundarya Krishnan, Melis Ozyildirim, Prathamesh Saraf, Halim Cagri Ates, Yuan Zhang, Hong Yu
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed in this paper to resolve references for various types of context, including previous turns and non-conversational entities. Large Language Models (LLMs) are utilized to create an effective system for reference resolution, by converting the task into a language modeling problem. The proposed method demonstrates significant improvements over existing systems across different types of references, with even the smallest model achieving absolute gains of over 5% for on-screen references. Performance is also benchmarked against GPT-3.5 and GPT-4, with the smallest model showing comparable results to GPT-4 and larger models outperforming it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how powerful language models can be used to help computers understand and handle different kinds of context. This includes not just what people have said before, but also things that are happening on their screens or in the background. The researchers found a way to use these language models to create an excellent system for resolving references, which is important for understanding different types of context. They tested this system and showed that it worked much better than previous systems, even outperforming some very powerful language models. |
Keywords
» Artificial intelligence » Gpt
