Natural Language Processing (NLP) and Reference Resolution in AI
Reference resolution is a big challenge in NLP. It’s about figuring out what a word or phrase refers to in a text. This is important for understanding different types of context, like in conversations or on-screen information.
Practical Solutions and Value
Researchers are improving large language models (LLMs) to better understand references, especially for non-conversational content. Models like MARRS and ReALM are tackling this by reconstructing the screen using parsed entities, tagging important parts, and fine-tuning the LLM to outperform existing models like GPT-3.5 and GPT-4. This provides a practical reference resolution system.
ReALM: An AI that Can ‘See’ and Understand Screen Context
Apple researchers have developed ReALM, an innovative approach that uses LLMs to understand references by encoding entity candidates as natural text. This model performs almost as well as the state-of-the-art LLM, GPT-4, despite having fewer parameters. It’s a great choice for practical reference resolution, even for on-screen references.
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Apple Researchers Present ReALM: An AI that Can ‘See’ and Understand Screen Context
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