alew1

Ah, thanks, was looking at the readme instead of the guide

Does Bend have linear types? I didn't see anything on them in a quick skim of the GUIDE file.

When you read something like this

> Change it to 300 and run it again. You get a different pitch and nothing breaks, because at this level there are no notes yet, just a number.

does it make sense? Or perhaps it just seems like something easy enough to skim past without worrying about whether it makes sense?

Before LMs, I would have assumed that a person intentionally wrote that sentence, and perhaps spent some time trying to understand what could possibly "break" if there were "notes" rather than "just a number." Now there is a simpler explanation: the sentence actually doesn't mean anything.

The article presents the fact that we appear to treat non-constituents (eg “in the middle of the”) as “units” to mean that language is more like “snapping legos together” than “building trees.”

But linguists have proposed the possibility that we store “fragments” to facilitate reuse—essentially trees with holes, or equivalently, functions that take in tree arguments and produce tree results. “In the middle of the” could take in a noun-shaped tree as an argument and produce a prepositional phrase-shaped tree as a result, for instance. Furthermore, this accounts for the way we store idioms that are not just contiguous “Lego block” sequences of words (like “a ____ and a half” or “the more ___, the more ____”). See e.g. work on “fragment grammars.”

Can’t access the actual Nature Human Behavior article so perhaps it discusses the connections.

The algorithms are not deterministic: they output a probability distribution over next tokens, which is then sampled. That’s why clicking “retry” gives you a different answer. An LM could easily (in principle) compute a 50/50 distribution when asked to flip a coin.

The taxpayer funding is for Harvard's research activities, not for its undergraduate teaching. The undergraduate teaching is funded by tuition (often paid in full by international students) and by returns on the endowment (including some earmarked for financial aid).

> they could vastly grow their class size without lowering standards

The issue isn't the quality of the students they are accepting, but the resources to educate and house them, including classroom space, dorms, and staff.

Didn't seem to realize that "Still more coherent than the OpenAI lineup" wouldn't make sense out of context. (The actual comment quoted there is responding to someone who says they'd name their models Foo, Bar, Baz.)

Any interpretation (including interpreting the inputs to the neural net as a "prompt") is "slapped on" in some sense—at some level, it's all just numbers being added, multiplied, and so on.

But I wouldn't call the probabilistic interpretation "after the fact." The entire training procedure that generated the LM weights (the pre-training as well as the RLHF post-training) is formulated based on the understanding that the LM predicts p(x_t | x_1, ..., x_{t-1}). For example, pretraining maximizes the log probability of the training data, and RLHF typically maximizes an objective that combines "expected reward [under the LLM's output probability distribution]" with "KL divergence between the pretraining distribution and the RLHF'd distribution" (a probabilistic quantity).

"Temperature" doesn't make sense unless your model is predicting a distribution. You can't "temperature sample" a calculator, for instance. The output of the LLM is a predictive distribution over the next token; this is the formulation you will see in every paper on LLMs. It's true that you can do various things with that distribution other than sampling it: you can compute its entropy, you can find its mode (argmax), etc., but the type signature of the LLM itself is `prompt -> probability distribution over next tokens`.

If I show you a strawberry and ask how many r’s are in the name of this fruit, you can tell me, because one of the things you know about strawberries is how to spell their name.

Very large language models also “know” how to spell the word associated with the strawberry token, which you can test by asking them to spell the word one letter at a time. If you ask the model to spell the word and count the R’s while it goes, it can do the task. So the failure to do it when asked directly (how many r’s are in strawberry) is pointing to a real weakness in reasoning, where one forward pass of the transformer is not sufficient to retrieve the spelling and also count the R’s.

One thought: If you want to be able to remove the static part, you could consider fine tuning without the static part. If you fine tune with, you’re teaching the model that the desired behavior occurs only in the presence of the static part (hence the going off the rails).

But the model ultimately still has to process the comma, the newline, the "job". Is the main time savings that this can be done in parallel (on a GPU), whereas in typical generation it would be sequential?

> Any question of personhood should be evaluated on the basis that we evaluate ourselves and others: by action and behavior, and not on whether sentience can or cannot arise from this or that configuration of code.

But what is action and behavior? We have a single interface to LaMDA: given a partially completed document, predict the next word. By iterating this process, we can make it predict a sentence, or paragraph. Continuing in this way, we could have it write a hypothetical dialogue between an AI and a human, but that is hardly a "canonical" way of using LaMDA, and there is no reason to identify the AI character in the document with LaMDA itself.

All this to say, I am not sure what you mean when you say it "claims sentience". What does it mean for it to "claim" something? Presumably, e.g., advanced image processing networks are as internally complex as LaMDA. But the interface to an advanced image processing network is, you put in an image, it gives out a list of objects and bounding boxes it detected in the image. What would it mean for such a network to claim sentience? LaMDA is no different, in that our interface to LaMDA does not allow us to ask it to "claim" things to us, only to predict likely completions of documents.