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Jason Corkill

jasoncorkill
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https://rapidata.ai

AI & ML interests

Human data annotation

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reacted to their post with 🔥 about 13 hours ago
🔥 It's out! We published the dataset for our evaluation of @OpenAI's new 4o image generation model. https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
posted an update about 13 hours ago
🔥 It's out! We published the dataset for our evaluation of @OpenAI's new 4o image generation model. https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
liked a dataset 1 day ago
Rapidata/Ideogram-V2_t2i_human_preference
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jasoncorkill's activity

reacted to their post with 🔥 about 13 hours ago
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🔥 It's out! We published the dataset for our evaluation of @OpenAI 's new 4o image generation model.

Rapidata/OpenAI-4o_t2i_human_preference

Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
posted an update about 13 hours ago
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540
🔥 It's out! We published the dataset for our evaluation of @OpenAI 's new 4o image generation model.

Rapidata/OpenAI-4o_t2i_human_preference

Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
liked 3 datasets 1 day ago
replied to their post 1 day ago
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The dataset will soon be published. In the meantime, check out the detailed benchmark here: 👉 rapidata.ai/leaderboard/image-models
reacted to their post with 👍🚀🔥 1 day ago
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🚀 First Benchmark of @OpenAI 's 4o Image Generation Model!

We've just completed the first-ever (to our knowledge) benchmarking of the new OpenAI 4o image generation model, and the results are impressive!

In our tests, OpenAI 4o image generation absolutely crushed leading competitors, including @black-forest-labs , @google , @xai-org , Ideogram, Recraft, and @deepseek-ai , in prompt alignment and coherence! They hold a gap of more than 20% to the nearest competitor in terms of Bradley-Terry score, the biggest we have seen since the beginning of the benchmark!

The benchmarks are based on 200k human responses collected through our API. However, the most challenging part wasn't the benchmarking itself, but generating and downloading the images:

- 5 hours to generate 1000 images (no API available yet)
- Just 10 minutes to set up and launch the benchmark
- Over 200,000 responses rapidly collected

While generating the images, we faced some hurdles that meant that we had to leave out certain parts of our prompt set. Particularly we observed that the OpenAI 4o model proactively refused to generate certain images:

🚫 Styles of living artists: completely blocked
🚫 Copyrighted characters (e.g., Darth Vader, Pokémon): initially generated but subsequently blocked

Overall, OpenAI 4o stands out significantly in alignment and coherence, especially excelling in certain unusual prompts that have historically caused issues such as: 'A chair on a cat.' See the images for more examples!
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posted an update 1 day ago
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1585
🚀 First Benchmark of @OpenAI 's 4o Image Generation Model!

We've just completed the first-ever (to our knowledge) benchmarking of the new OpenAI 4o image generation model, and the results are impressive!

In our tests, OpenAI 4o image generation absolutely crushed leading competitors, including @black-forest-labs , @google , @xai-org , Ideogram, Recraft, and @deepseek-ai , in prompt alignment and coherence! They hold a gap of more than 20% to the nearest competitor in terms of Bradley-Terry score, the biggest we have seen since the beginning of the benchmark!

The benchmarks are based on 200k human responses collected through our API. However, the most challenging part wasn't the benchmarking itself, but generating and downloading the images:

- 5 hours to generate 1000 images (no API available yet)
- Just 10 minutes to set up and launch the benchmark
- Over 200,000 responses rapidly collected

While generating the images, we faced some hurdles that meant that we had to leave out certain parts of our prompt set. Particularly we observed that the OpenAI 4o model proactively refused to generate certain images:

🚫 Styles of living artists: completely blocked
🚫 Copyrighted characters (e.g., Darth Vader, Pokémon): initially generated but subsequently blocked

Overall, OpenAI 4o stands out significantly in alignment and coherence, especially excelling in certain unusual prompts that have historically caused issues such as: 'A chair on a cat.' See the images for more examples!
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reacted to their post with 🧠👍🔥😎👀 11 days ago
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3780
At Rapidata, we compared DeepL with LLMs like DeepSeek-R1, Llama, and Mixtral for translation quality using feedback from over 51,000 native speakers. Despite the costs, the performance makes it a valuable investment, especially in critical applications where translation quality is paramount. Now we can say that Europe is more than imposing regulations.

Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.

Rapidata/Translation-deepseek-llama-mixtral-v-deepl
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posted an update 11 days ago
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At Rapidata, we compared DeepL with LLMs like DeepSeek-R1, Llama, and Mixtral for translation quality using feedback from over 51,000 native speakers. Despite the costs, the performance makes it a valuable investment, especially in critical applications where translation quality is paramount. Now we can say that Europe is more than imposing regulations.

Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.

Rapidata/Translation-deepseek-llama-mixtral-v-deepl
  • 1 reply
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liked a dataset 12 days ago
reacted to their post with 👀 17 days ago
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2185
Benchmarking Google's Veo2: How Does It Compare?

The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.

Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.

We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.

Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models

Rapidata/text-2-video-human-preferences-veo2
posted an update 17 days ago
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2185
Benchmarking Google's Veo2: How Does It Compare?

The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.

Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.

We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.

Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models

Rapidata/text-2-video-human-preferences-veo2
liked a dataset 18 days ago