Cloud Xu @xucloud - Twitter Profile

Cloud Xu @xucloud

10 months ago

As a first day user, I won’t be suppressed on this result.

Olivia Moore

@omooretweets

10 months ago

Sora is still picking up downloads, but the early retention data (shown below vs TikTok) looks fairly weak What this says to me is the model is truly viral, and there’s a base of power users making + exporting Sora videos …but, most users aren’t sticking on the app

omooretweets's tweet photo. Sora is still picking up downloads, but the early retention data (shown below vs TikTok) looks fairly weak

What this says to me is the model is truly viral, and there’s a base of power users making + exporting Sora videos

…but, most users aren’t sticking on the app https://t.co/brJVdRTkoM

36

145

16

76

60K

0

28

xucloud retweeted

Olivia Moore

@omooretweets

10 months ago

Sora is still picking up downloads, but the early retention data (shown below vs TikTok) looks fairly weak What this says to me is the model is truly viral, and there’s a base of power users making + exporting Sora videos …but, most users aren’t sticking on the app

36

145

16

76

60K

Cloud Xu @xucloud

over 2 years ago

It can be a good missing piece.

Nathan Benaich

@nathanbenaich

over 2 years ago

llm routing systems - which decide which llm to use for what task - are getting more important, esp. if open source keeps flourishing yet there's no standard evaluation method for routing systems...enter ROUTERBENCH As the range of applications for Large Language Models (LLMs) continues to grow, the demand for effective serving solutions becomes increasingly critical. Despite the versatility of LLMs, no single model can optimally address all tasks and applications, particularly when balancing performance with cost. This limitation has led to the development of LLM routing systems, which combine the strengths of various models to overcome the constraints of individual LLMs. Yet, the absence of a standardized benchmark for evaluating the performance of LLM routers hinders progress in this area. To bridge this gap, we present ROUTERBENCH, a novel evaluation framework designed to systematically assess the efficacy of LLM routing systems, along with a comprehensive dataset comprising over 405k inference outcomes from representative LLMs to support the development of routing strategies. We further propose a theoretical framework for LLM routing, and deliver a comparative analysis of various routing approaches through ROUTERBENCH, highlighting their potentials and limitations within our evaluation framework. This work not only formalizes and advances the development of LLM routing systems but also sets a standard for their assessment, paving the way for more accessible and economically viable LLM deployments. The code and data are available at https://t.co/EwlUE61bIn.