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Yulia Khalus – Computational Linguist @ Grammarly

In this talk, we will examine how LLM outputs are evaluated by potential end users versus professional linguist-annotators, as two ways of ensuring alignment with real-world user needs and expectations. We will compare the two approaches, highlight the advantages and recurring pitfalls of user-driven annotation, and share the mitigation techniques we have developed from our own experience.

llms linguistic annotation NLP user studies
Lera Lakusta – computational linguist @ Grammarly

How can we influence quality during the prompt creation stage, as well as how to work with already-generated text—improving it, identifying errors, and filtering out undesirable results. We'll explore linguistic approaches that help achieve better, more controlled outcomes from LLMs.

llms prompt engineering text quality linguistic approaches
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