TruthfulQA VS safari

Dataset: https://github.com/sylinrl/TruthfulQA

this is a great point.

do you know of any benchmarks doing this today?

given the acute need to evaluate models on contextual factuality, we're exploring how to create a benchmark for this purpose but prefer existing benchmarks if possible.

openai's truthfulqa[0] is close but does not focus on contextual factuality and targets a much harder problem of absolute truth.

if none exist, and people are interested in contributing, please reach out.

[0] https://github.com/sylinrl/TruthfulQA

I agree they show that you can brute-force mimick uncertainty estimates to some degree, and that the model is generally well calibrated (though on what is basically a set of trivia questions, so YMMV)... yet:

Code for https://arxiv.org/abs/2109.07958 found: https://github.com/sylinrl/TruthfulQA

> Also, we know that transformers can scale

Do we have strong evidence that other models don't scale or have we just put more time into transformers?

Convolutional resnets look to scale on vision and language: (cv) https://arxiv.org/abs/2301.00808, (cv) https://arxiv.org/abs/2110.00476, (nlp) https://github.com/HazyResearch/safari

MLPs also seem to scale: (cv) https://arxiv.org/abs/2105.01601, (cv) https://arxiv.org/abs/2105.03404

I mean I don't see a strong reason to turn away from attention as well but I also don't think anyone's thrown a billion parameter MLP or Conv model at a problem. We've put a lot of work into attention, transformers, and scaling these. Thousands of papers each year! Definitely don't see that for other architectures. The ResNet Strikes back paper is a great paper for one reason being that it should remind us all to not get lost in the hype and that our advancements are coupled. We learned a lot of training techniques since the original ResNet days and pushing those to ResNets also makes them a lot better and really closes the gaps. At least in vision (where I research). It is easy to railroad in research where we have publish or perish and hype driven reviewing.

After a very quick read, that's my understanding too: It's just KNN search. So I agree on points 1-3. When something works well, I don't care much about point 4.

I've had only mixed success with KNN search. Maybe I haven't done it right? Nothing seems to work quite as well for me as explicit token-token interactions by some form of attention, which as we all know is too costly for long sequences (O(n²)). Lately I've been playing with https://github.com/hazyresearch/safari , which uses a lot less compute and seems promising. Otherwise, for long sequences I've yet to find something better than https://github.com/HazyResearch/flash-attention for n×n interactions and https://github.com/glassroom/heinsen_routing for n×m interactions. If anyone here has other suggestions, I'd love to hear about them.

Code: https://github.com/HazyResearch/safari

the code is here https://github.com/hazyresearch/safari you should try it and let us know your verdict.