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Users or experts? Navigating the trade-offs in LLM quality annotation
2025-08-26 · 19:00
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. |
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Beyond generation: How to get high-quality text from LLMs
2025-08-26 · 16:30
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. |
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