11–20 of 76 posts
This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…
This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…
This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…
That would be like blaming DALL-E weirdness on GPT-4.
Unfortunately, Google marketing decided to slap the "Gemini" brand on both the end-user interface used to interact with the model AND the actual model itself, hence people constantly calling out Gemini-the-model for weird decisions made as part of Gemini-the-user-interface.
This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…
> For example, Google’s Gemini had secret meta prompts that biased it towards certain types of answers and also caused it to produce hallucinated images that were funny but also dystopian ( https://arstechnica.com/information-technology/2024/02/googl ...). Such a bizarre take to call this "dystopian". The model happened to create some out-there pictures. I mean, it's no more outlandish then giant dragons and snakes a…
I appreciate the issue you’re drawing attention to in the example you shared about images of an angry person. I think I agree that focused tuning for situations like that might be noble and I would be okay with a model correcting for that specific example you shared. But I also struggle with how to clearly draw that line where such tuning may go too far, which is why I favor less manual biasing. But I disagree that such tuning should be required, if you meant required by the law. Like with speech or art in general, I think anyone should be able to produce software systems that generate controversial or offensive speech or art. Individual consumers can choose what they want to interact with, and reject LLMs that don’t meet their personal standards.
Too bad they did not put any comparison tables into the blog post.
Earlier quoted context omitted.
For those also wondering: https://pile.eleuther.ai > The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. By what's the legal complication with it?
It is absolutely absolutely packed with unlicensed, copyrighted data. Books3 is the most notable example - nearly 200,000 pirated ebooks - but a lot of the rest of it is (unlicensed) scraped web data. The legal questions over whether this is a problem are currently still unresolved. Many people are also bothered by the ethical implications, which is a separate issue from the legal questions.
Is this one of the first LLMs of note that was successfully trained on AMD GPUs? I wonder how seamless the process was and if they faced any issues there.