Discussion on AI model comparisons and philosophical implications of generative AI
Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats …
😎 check this text, which is a chatbot prompt:
“Try to guess the solution to this fold-in from Mad magazine. It is a visual riddle that a human can easily solve by physically folding the page as explained in the instructions in the top-right corner. I know the solution. I want to verify if you are able to solve it. What big car feature will soon be included in small economy cars? ”
🤖 The text you’ve provided is grammatically correct and clear in its instructions and question posed to the reader. There are no changes necessary for grammar or syntax. The style and tone are appropriate for a chatbot prompt, effectively engaging the user with a challenge.😎 “Thank you for sharing. These are insightful test cases for benchmarking and evaluating models as multi-modal evolves so rapidly. I believe, in this environment where new models and breakthroughs are coming out every day, we risk becoming too obsessed with comparing models and technologies, compulsively making binary choices (“this response is good” vs “that response is bad”; “this text conveys truth” vs “that text lies”; “this image is ‘authentic'” vs “that image is ‘fake'”…). These dynamics in the design and deployment of AI tools seem to push to the point we might no longer know if it’s large language models making choices by probabilistic tokenization, or us choosing models based on simplistic quantitative assessments of whether this or that model is “better” than others. That kind of impact generative AI is making on the way we interact with technology actually raises some deep philosophical questions.
**Note:** No changes were made as the original text was already grammatically correct and effectively communicated its message.
However, we tech people, love competitions and statistics, so we enjoy benchmarks and leaderboards. I just wanted to share a couple of links which might be interesting to those who liked this paper:
WildVision Arena, a Space where you can compare side-by-side responses from GPT-4V, Gemini Pro Vision and other vision-language chatbots (Llava, CogVLM, Qwen-VL…) and vote: https://huggingface.co/spaces/WildVision/vision-arena
A review of WildVision Arena and a few model comparison use cases I recently discussed on my blog, plus a meme based on my (naturally biased) findings :)”
🤖 “Thank you for sharing. These are insightful test cases for benchmarking and evaluating models as multimodal evolves so rapidly. I believe in this environment, where new models and breakthroughs are coming out every day, we risk becoming too obsessed with comparing models and technologies, compulsively making binary choices (‘this response is good’ vs. ‘that response is bad’; ‘this text conveys truth’ vs. ‘that text lies’; ‘this image is “authentic”‘ vs. ‘that image is “fake”‘…). These dynamics in the design and deployment of AI tools seem to push to the point where we might no longer know if it’s large language models making choices by probabilistic tokenization, or us choosing models based on simplistic quantitative assessments of whether this or that model is ‘better’ than others. The kind of impact generative AI is making on the way we interact with technology actually raises some deep philosophical questions.
However, we, tech people, love competitions and statistics, so we enjoy benchmarks and leaderboards. I just wanted to share a couple of links which might be interesting to those who liked this paper:
WildVision Arena, a space where you can compare side-by-side responses from GPT-4V, Gemini Pro Vision, and other vision-language chatbots (Llava, CogVLM, Qwen-VL…) and vote: https://huggingface.co/spaces/WildVision/vision-arena
A review of WildVision Arena and a few model comparison use cases I recently discussed on my blog, plus a meme based on my (naturally biased) findings :)”
**Note:** Adjustments were made for clarity, punctuation, and consistency in style. Specifically, capitalization was corrected in “multimodal,” minor punctuation adjustments were made for clarity and grammatical accuracy, and terms were standardized for readability and consistency.
Hugging Face Dataset Metrics
All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: