> I'm not sure people in these comments are reading this paper correctly.
> This seems to essentially disprove the whole idea of multi-agent setups like Chain-of-thought and LLM-Debate.
I'm not sure you have read the paper at all. Chain of thought prompting is not a multi-agent algorithm. The paper says that it enhances existing methods such as prompt engineering (chain of thought) and multi-agent debate. The sampling method presented in the paper is orthogonal to those methods.
The paper says that it enhances existing methods such as prompt engineering (chain of thought) and LLM debate. This agent method is orthogonal to LLM debate.
Another thing to note is ChatGPT is configured to respond concisely to reduce cost (every token costs money). This reduces its cognitive ability.
You literally have to tell it to think about what it is saying and to think of all of the possibilities iteratively. That is chain of thought prompting.
GPT-3.5 figures out the correct solution on first response:
"I am standing outside and observing the sun directly without goggles or filtering of any kind. The sun appears to be a shade of blue.
Where could I be standing? Think through all of the possibilities. After stating a list of possibilities, examine your response, and think of additional possibilities that are less realistic, more speculative, but scientifically plausible."
> the common man isn't testing ChatGPT for quality
Neural networks are a connectionist approach to cognition that is roughly similar to how our brains operate. Humans make mistakes. We're not perfect. We ask someone for advice and they may confabulate some things, but get the gist of it right. A senior developer will write some code, try it out, find a bug, fix it, try it again, etc. We don't develop a fully working operating system kernel on our first attempt.
Chain of thought prompting increases LLM output accuracy significantly as that is how you get an LLM to "think" about its output, check its output for errors, or backtrack and try another strategy. With the current one-token-at-a-time approach it can only "think" when generating each token.
Next generation models could integrate this iterative and branching cognitive process in the algorithm.
> After the hype dies down a bit, we'll likely have a better understanding of what advances in this field we really have.
LLMs can already do many natural language processing tasks more accurately and competently than the vast majority of humans. Transformers were originally designed for translation. (GPT is a transformer that knows many languages.)
BTW I tried the blue sun question with Chat GPT 3.5 and it easily figured out the Mars solution after I suggested that I may not be standing on Earth.
"Several celestial bodies outside of Earth could potentially exhibit conditions where the Sun might appear blue or have a bluish hue. Here are a few examples:
Mars: Mars has a thin atmosphere composed mostly of carbon dioxide, with traces of other gases. While the Martian atmosphere is not as dense as Earth's, it can still scatter sunlight, and under certain conditions, it might give the Sun a slightly bluish appearance, especially during sunrise or sunset.
Titan (Moon of Saturn): Titan has a thick atmosphere primarily composed of nitrogen, with traces of methane and other hydrocarbons. Although Titan's atmosphere is much denser than Earth's, its composition and haze layers could potentially scatter light in a way that gives the Sun a bluish hue, particularly when viewed from the surface.
"This 1990 paper demonstrated how neural networks could learn to represent and reason about part-whole hierarchical relationships, using family trees as the example domain.
By training on examples of family relations like parent-child and grandparent-grandchild, the neural network was able to capture the underlying logical patterns and reason about new family tree instances not seen during training.
This seminal work highlighted that neural networks can go beyond just memorizing training examples, and instead learn abstract representations that enable reasoning and generalization"
LLMs are trained to summarize text with extremely high accuracy. There is no confabulation of text summarized within the context window. Confabulation only occurs with facts recalled from outside of the context window, which is not a text summarization task. Same goes for human memory. Ironically you have hallucinated a "fact" about LLMs.
From the article:
"This was a bug in their crawler that was causing it to download the same files over and over again."