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Natural Language Processing (NLP) and Reference Resolution in AI
Reference resolution presents a critical challenge within NLP, as it involves determining the antecedent or referent of a word or phrase within a text. This understanding is essential for successfully handling different types of context, from dialogue turns to on-screen entities or background processes.
Practical Solutions and Value
Researchers are working on improving the ability of large language models (LLMs) to resolve references, especially for non-conversational entities. Models like MARRS and ReALM are addressing this challenge by reconstructing the screen using parsed entities, tagging the important parts, and fine-tuning the LLM to outperform existing models like GPT-3.5 and GPT-4 in various datasets, providing a practical reference resolution system.
ReALM: An AI that Can ‘See’ and Understand Screen Context
Apple researchers have proposed ReALM, a groundbreaking approach that uses LLMs to perform reference resolution by encoding entity candidates as natural text. This model outperforms previous approaches, performing almost as well as the state-of-the-art LLM, GPT-4, despite having fewer parameters. It is an ideal choice for practical reference resolution, even for onscreen references.
Check out the paper for more details on this research.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Apple Researchers Present ReALM: An AI that Can ‘See’ and Understand Screen Context
- MarkTechPost
- Twitter – @itinaicom
