Recent comments in /f/MachineLearning

Deep-Station-1746 t1_je8u12c wrote

Reply to [R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention by floppy_llama

This is interesting - compared to LoRa, it allows LLaMA to also accept images as inputs. And, I believe it is orthogonal to using LoRa. Meaning, they possibly can be used together. I'm unsure about the training stability though. I know that LoRa training allows ridiculously high learning rates (1e-5 for Text encoder), especially for dreambooth. Using LoRa for the frozen weights + LLaMA adapter is an interesting thing to explore.

Edit: spelling

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3_Thumbs_Up t1_je8tvtc wrote

Reply to comment by sdmat in [R] The Debate Over Understanding in AI’s Large Language Models by currentscurrents

>We can test with things that are highly unlikely to be in the training data.

We can also test things where theres an infinite amount of alternatives so that memorization would be impossible.

If GPT could solve every arithmetic problem thrown at it, then it's obvious that it has developed some understanding of arithmetic, as it's simply impossible to memorize the answer for every possible problem.

However, the fact that it fails on arithmetic of large numbers could be an indication that it doesn't understand, but failure could also be caused by other factors, such as lack of enough working memory or similar (humans would fail at multiplying large numbers in their head as well).

So I think one could prove understanding, but proving lack of understanding seems harder.

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Cantareus t1_je8pk04 wrote

Reply to comment by yaosio in [Discussion] IsItBS: asking GPT to reflect x times will create a feedback loop that causes it to scrutinize itself x times? by RedditPolluter

There's no self-reflecting happening when the request to self-reflect is in the prompt. The improvement happens because the expected output after asking it to self-reflect is a more thought out response. You can get a type of reflecting by pasting the output back into the prompt with your own feedback.

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sdmat t1_je8p59n wrote

Reply to comment by NotDoingResearch2 in [R] The Debate Over Understanding in AI’s Large Language Models by currentscurrents

I think we can safely say this is:

> it's essential to note that relying solely on jeans for habitat survivability would not be sufficient.

I don't have a degree in exojeanology, but the ideas all seem to at least be at the level of smart generalist brainstorming.

Specifically:

  • Contextually appropriate - these are plausibly relevant to the needs of a Martian colony
  • Nontrivial - no "wear them as clothing" (GPT3.5 did this)
  • Logical and well articulated - each is a clearly expressed and internally consistent concept
  • Passes the "common sense" test - no linguistically valid statements that are ridiculous if you have general knowledge of the world. E.g. "Use jeans to signal ships in orbit", or GPT3.5's suggestion to use jeans as radiation shielding because denim is a thick fabric.

They aren't necessarily good ideas in the sense that NASA should be writing this down. But that isn't the point.

I would argue that behaviourally GPT4 demonstrates a great deal of understanding here and a notable lack of the "brittleness and unhumanlike errors" that 3.5 shows on the same question.

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Purplekeyboard t1_je8m61n wrote

Reply to comment by currentscurrents in [R] The Debate Over Understanding in AI’s Large Language Models by currentscurrents

> LLMs likely have a type of understanding, and humans have a different type of understanding.

Yes, this is more of a philosophy debate than anything else, hinging on the definition of the word "understanding". LLMs clearly have a type of understanding, but as they aren't conscious it is a different type than ours. Much as a chess program has a functional understanding of chess, but isn't aware and doesn't know that it is playing chess.

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disastorm t1_je8lm7w wrote

Reply to [D] Simple Questions Thread by AutoModerator

I have a question about reinforcement learning, or more specifically gym-retro ( i know gym is pretty old now I guess ).

In the case of gym-retro, if you give a reward to the AI, are they actually looking at a set of variables and saying like "oh I pressed this button while all of these variables were these values and got this reward, so I should press it when all these variables are similar" or are they just saying like "oh I pressed this button and got this reward, so I should press it more often"?

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hailfire27 t1_je8l7id wrote

Reply to comment by ghostfaceschiller in [R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention by floppy_llama

I think he's talking about how during conversations, there are different cognitive levels to a conversation. You are basically having a conversation with yourself about what to say and remembering things to talk about, while at the same time considering the context of the situation, such as the environment or activity.

So he's saying for a model like this, would it be possible to tune the model so that it is able to give better answers in a conversation.

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Purplekeyboard t1_je8l78y wrote

Reply to comment by currentscurrents in [R] The Debate Over Understanding in AI’s Large Language Models by currentscurrents

The point is that GPT-3 and GPT-4 can synthesize information to produce new information.

One question I like to ask large language models is "If there is a great white shark in my basement, is it safe for me to be upstairs?" This is a question no one has ever asked before, and answering the question requires more than just memorization.

Google Bard answered rather poorly, and said that I should get out of the house or attempt to hide in a closet. It seemed to be under the impression that the house was full of water and that the shark could swim through it.

GPT-3, at least the form of it I used when I asked it, said that I was safe because sharks can't climb stairs. Bing Chat, using GPT-4, was concerned that the shark could burst through the floorboards at me, because great white sharks can weigh as much as 5000 pounds. But all of these models are forced to put together various bits of information on sharks and houses in order to try to answer this entirely novel question.

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LetGoAndBeReal t1_je8j7hw wrote

Reply to comment by elbiot in [D] The best way to train an LLM on company data by jaxolingo

The key word in that OpenAI link is “examples”. It says “more examples” and not “more knowledge”, because it’s referring to few shot training, which is about conditioning rather than providing new data.

In other words, if you want to get the model to classify sentiment of user comments as positive or negative, you can provide several examples in the prompt of both positive and negative comments. Fine-tuning allows you to provide many more such examples to the model than can fit in a prompt.

The key point is that through fine-tuning these examples can condition the model to classify sentiment but do not cause new facts to be absorbed by the model. You cannot get new facts to be readily absorbed through fine-tuning, which is why the OP should not look to fine-tuning to endow the model with the external dataset they want to use for question answering.

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