Jason Corkill
jasoncorkill
AI & ML interests
Human data annotation
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replied to their post 2 days ago
posted an update 2 days ago
Post
70
Are you an AI Researcher, Enthusiast, Founder or else and live in or close by to Zürich? Drop in for an Aperol Spritz and Euromaxx on our Balcony on the 14th of August.
- 1 reply
posted an update 9 months ago
Post
4747
Do you remember https://thispersondoesnotexist.com/ ? It was one of the first cases where the future of generative media really hit us. Humans are incredibly good at recognizing and analyzing faces, so they are a very good litmus test for any generative image model.
But none of the current benchmarks measure the ability of models to generate humans independently. So we built our own. We measure the models ability to generate a diverse set of human faces and using over 20'000 human annotations we ranked all of the major models on their ability to generate faces. Find the full ranking here:
https://app.rapidata.ai/mri/benchmarks/68af24ae74482280b62f7596
We have release the full underlying data publicly here on huggingface: Rapidata/Face_Generation_Benchmark
But none of the current benchmarks measure the ability of models to generate humans independently. So we built our own. We measure the models ability to generate a diverse set of human faces and using over 20'000 human annotations we ranked all of the major models on their ability to generate faces. Find the full ranking here:
https://app.rapidata.ai/mri/benchmarks/68af24ae74482280b62f7596
We have release the full underlying data publicly here on huggingface: Rapidata/Face_Generation_Benchmark
replied to their post about 1 year ago
Perhaps we can provide a couple of thousand human annotations
replied to their post about 1 year ago
Interesting, what kind of data are you collecting?
replied to their post about 1 year ago
Funny, we also noticed that these models will almost always revert to the Question - Answer Style Joke if not prompted otherwise.
reacted to their post with 👀 about 1 year ago
Post
3290
"Why did the bee get married?"
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
- 7 replies
posted an update about 1 year ago
Post
3290
"Why did the bee get married?"
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
- 7 replies
reacted to their post with 🧠❤️🚀 about 1 year ago
Post
2445
Imagine you could have an Image Arena score equivalent at each checkpoint during training. We released the first version of just that:
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
posted an update about 1 year ago
Post
2445
Imagine you could have an Image Arena score equivalent at each checkpoint during training. We released the first version of just that:
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
Crowd-Eval
Add one line of code to your training loop and you will have a new real human loss curve in your W&B dashboard.
Thousands of real humans from around the world rating your model in real time at the cost of a few dollars per checkpoint is a game changer.
Check it out here: https://github.com/RapidataAI/crowd-eval
First 5 people to put it in their loop get 100'000 human responses for free! (ping me)
replied to their post about 1 year ago
Good catch :) yes, we uploaded them shortly after!
replied to their post about 1 year ago
Hey Jackson, can you please elaborate?
reacted to their post with 👍👀🔥🚀 about 1 year ago
Post
4030
Benchmark Update: @google Veo3 (Text-to-Video)
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
- 5 replies
posted an update about 1 year ago
Post
4030
Benchmark Update: @google Veo3 (Text-to-Video)
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
- 5 replies
reacted to their post with ❤️ about 1 year ago
Post
2887
🔥 Hidream I1 is online! 🔥
We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.
It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.
Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference
What model should we benchmark next?
We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.
It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.
Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference
What model should we benchmark next?