Fireside Chat "Evaluating quality in LLM models pre-production" with Guy Rom
Fireside chat on evaluating quality in LLM models pre-production.
Event
Activities tracked
2
We are thrilled to host and launch this event in cooperation with the Meetup.ai Berlin community.๐ฅ โ Registration: to attend the meetup, please RSVP on the Meetup.ai Berlin community page โก๏ธ here โฌ ๏ธ.
๐ฅ Agenda: 17:30 Doors open: Time for mingling and networking with fellows; drinks will be served 18:00 Opening notes 18:10 Talk "Prompting for Production: Ensuring the Quality of LLM Outputs in Product Features at Grammarly" by Yulia Khalus, Computational Linguist at Grammarly 18:40 Break 18:45 Fireside Chat "Evaluating quality in LLM models pre-production" with Guy Rom, NLP consultant, ex-ML Researcher at Google Research 19:15 Community round pitch 19:30 Networking 21:00 Meetup ends
๐ฅ Prompting for Production: Ensuring the Quality of LLM Outputs in Product Features at Grammarly ๐ Yulia Khalus - Computational Linguist at Grammarly ๐ LLMs have unlocked new opportunities in NLP with their possible applications. Features that used to take months to be planned and developed now require a day to be prototyped. But how can we make sure that a successful prototype will turn into a high-quality feature useful for millions of customers? In this talk, we will explore real examples of the challenges that arise when ensuring the quality of LLM outputs and how we address them at Grammarly.
โ Where: In-person, Grammarly Berlin hub โ When: Thursday, October 24 โ Language: English โ Registration: to attend the meetup, please RSVP on the Meetup.ai Berlin community. โก๏ธ here โฌ ๏ธ
Sessions & talks
Showing 1โ2 of 2 ยท Newest first
Fireside chat on evaluating quality in LLM models pre-production.
LLMs have unlocked new opportunities in NLP with their possible applications. Features that used to take months to be planned and developed now require a day to be prototyped. But how can we make sure that a successful prototype will turn into a high-quality feature useful for millions of customers? In this talk, we will explore real examples of the challenges that arise when ensuring the quality of LLM outputs and how we address them at Grammarly.