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I don't really understand the benchmarking aspect researchers are touting. The public never cared about LLMs until they had a proper conversation with one. You

by shmatt 4y ago

I don't really understand the benchmarking aspect researchers are touting. The public never cared about LLMs until they had a proper conversation with one. You can beat GPT3 at any benchmark you'd like, but if you can't get people that "feeling" when chatting with your model, is it worth anything? In the future there's going to have to be a way to benchmark the "human-ness" or "intrigue" or "feistiness" of a model to show us if its getting better at what we want

rnosov 4y ago
GPT-3 performed really well on synthetic benchmarks. It was later made palatable for general public consumption. You might say that a LLM needs to be good on synthetic benchmarks first before you can make public facing chatbots with it.
basch 4y ago
The question is, where does this "human-ness" lie? In the initial neural network, the training data, or the supervised reinforcement? In theory, a significantly smaller neural network that outputs at nearly as good a quality, should be able to chew through training data, and its re-enforcement process, much faster and cheaper, right? A more generalized, lower parameter model, is almost always preferred, as long as it works? If the human feeling is all boltonable to the neural net later, there is no reason to discount this component as lacking potential to exceed current models.
- visarga 4y ago
  I think it's the feedback from RLHF that is mostly responsible, but it only works if the base model is large. Never seen a small model doing good conversation. They can do ok for classification and open book question answering, but generating long form coherent text is hard.
antoineMoPa 4y ago
I guess one technique is to train a model on various language model outputs to classify these as good/bad/intriguing/robot-sounding/repetitive/etc. A human can tag the answers for the training dataset. Then, we can use this model to compare different LLMs and optimize new models - could be with genetic algorithms or just a human tweaking the model - so that the LLM maximizes whatever we want.
- visarga 4y ago
  You just rediscovered RLHF - reinforcement learning from human preferences. That's the last stage of training for chatGPT, but uses RL instead of supervised learning.
skybrian 4y ago
Often articles show up on Hacker News that are meant for other researchers, not the general public. Not everything is a product. Benchmarks are used as a way to show that a particular machine learning technique does better at some task. It’s a way for researchers to show they’re making progress that will be legible to other researchers. You can’t publish a paper saying “we tried it out and we think it’s better.”
dragonwriter 4y ago
The techniques to go from a basic GPT-like model to a conversational agent are largely published and should be reproducible, open-source base models are starting points for that work that are unencumbered and available. This is important for researchers and implementers, not (immediately, ay least) end users.

by shmatt 4y ago

rnosov 4y ago

GPT-3 performed really well on synthetic benchmarks. It was later made palatable for general public consumption. You might say that a LLM needs to be good on synthetic benchmarks first before you can make public facing chatbots with it.

basch 4y ago

The question is, where does this "human-ness" lie? In the initial neural network, the training data, or the supervised reinforcement? In theory, a significantly smaller neural network that outputs at nearly as good a quality, should be able to chew through training data, and its re-enforcement process, much faster and cheaper, right? A more generalized, lower parameter model, is almost always preferred, as long as it works? If the human feeling is all boltonable to the neural net later, there is no reason to discount this component as lacking potential to exceed current models.

visarga 4y ago
I think it's the feedback from RLHF that is mostly responsible, but it only works if the base model is large. Never seen a small model doing good conversation. They can do ok for classification and open book question answering, but generating long form coherent text is hard.

antoineMoPa 4y ago

I guess one technique is to train a model on various language model outputs to classify these as good/bad/intriguing/robot-sounding/repetitive/etc. A human can tag the answers for the training dataset. Then, we can use this model to compare different LLMs and optimize new models - could be with genetic algorithms or just a human tweaking the model - so that the LLM maximizes whatever we want.

visarga 4y ago
You just rediscovered RLHF - reinforcement learning from human preferences. That's the last stage of training for chatGPT, but uses RL instead of supervised learning.

skybrian 4y ago

Often articles show up on Hacker News that are meant for other researchers, not the general public. Not everything is a product. Benchmarks are used as a way to show that a particular machine learning technique does better at some task. It’s a way for researchers to show they’re making progress that will be legible to other researchers. You can’t publish a paper saying “we tried it out and we think it’s better.”

dragonwriter 4y ago

The techniques to go from a basic GPT-like model to a conversational agent are largely published and should be reproducible, open-source base models are starting points for that work that are unencumbered and available. This is important for researchers and implementers, not (immediately, ay least) end users.