[HN Gopher] When do you need chain-of-thought prompting for Chat...
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       When do you need chain-of-thought prompting for ChatGPT?
        
       Author : belter
       Score  : 84 points
       Date   : 2023-04-09 14:30 UTC (8 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | siavosh wrote:
       | Is COT considered a deep area of engineering/research or is it
       | considered as simply a temporary hack that will be obsolete in
       | the near or medium term?
        
         | [deleted]
        
         | travisjungroth wrote:
         | I don't know of a consensus for it to be "considered" one way
         | or another. I think explicitly asking for COT is temporary, and
         | it will move to a layer that's encapsulated from most users. It
         | will persist there as long as long as the language layer is
         | doing the "thinking". If the language layer is completely
         | offloading that and just translating, it won't be needed.
         | 
         | I've been explaining it to people as "if ChatGPT can think, it
         | can only think out loud". Because of the way LLMs were trained,
         | nudging it to do this will continue to be valuable.
         | 
         | Most writing about anything difficult is product, not process.
         | Articles get drafts before being published. People think about
         | answers before writing them down. How to Solve It does a great
         | job explaining this about math problems. The steps to the proof
         | are not the steps to creating the proof.
         | 
         | So when you go to solve a problem by mimicking the solutions to
         | problems, something is missing. People will either need to
         | train LLMs to do it, or continue to ask for it in prompts.
        
         | Ozzie_osman wrote:
         | Despite the fancy name (Chain Of Thought) and multiple arxiv
         | papers, I'd definitely consider it a temporary hack that the
         | models will overcome as they get more powerful and as they are
         | better-trained to avoid hallucination. From this paper, it
         | looks like ChatGPT may have already overcome it for some types
         | of questions.
         | 
         | What may be here to stay might be more task-specific prompting
         | to break a problem down. For example, a variation might be
         | "Let's think step by step, making sure we do or consider X
         | before we do Y".
         | 
         | By way of analogy, when my toddler was really young, if he
         | couldn't figure something out, I'd ask him to break it down
         | into steps (very similar to general Cot prompting). Now, he
         | rarely needs that, but still needs more occasional, task-
         | specific "prompting", eg "Let's think about the weather, and
         | what we're going to be doing, before deciding what to wear".
        
       | wiradikusuma wrote:
       | Does anyone know a good tutorial for producing good prompts? I
       | forgot the document name, but last time there was a "bible" for
       | producing good results from text-to-image.
        
         | splatzone wrote:
         | Are you referring to the DALLE-2 prompt book?
         | https://dallery.gallery/wp-content/uploads/2022/07/The-DALL%...
        
       | belter wrote:
       | "...Chain-of-Thought (CoT) prompting can effectively elicit
       | complex multi-step reasoning from Large Language Models~(LLMs).
       | For example, by simply adding CoT instruction ``Let's think step-
       | by-step'' to each input query of MultiArith dataset, GPT-3's
       | accuracy can be improved from 17.7\% to 78.7\%. However, it is
       | not clear whether CoT is still effective on more recent
       | instruction finetuned (IFT) LLMs such as ChatGPT. Surprisingly,
       | on ChatGPT, CoT is no longer effective for certain tasks such as
       | arithmetic reasoning while still keeping effective on other
       | reasoning tasks. Moreover, on the former tasks, ChatGPT usually
       | achieves the best performance and can generate CoT even without
       | being instructed to do so...
       | 
       | ...Hence, it is plausible that ChatGPT has already been trained
       | on these tasks with CoT and thus memorized the instruction so it
       | implicitly follows such an instruction when applied to the same
       | queries, even without CoT. Our analysis reflects a potential risk
       | of overfitting/bias toward instructions introduced in IFT, which
       | becomes more common in training LLMs..."
        
         | amrb wrote:
         | I found to very difficult to set goals with gpt-turbo, maybe
         | gpt4 will be a good test when I get access.
         | 
         | Also I'm thinking would be interesting to train a LoRa, using
         | synthetic prompts.
        
         | nerdponx wrote:
         | It's really weird to me that we now have research papers
         | dedicated to exploring the emergent behavior of systems that
         | other people built, but where the researchers can only
         | speculate about what the builders actually did to build the
         | thing. "Open" AI indeed.
        
           | rmbyrro wrote:
           | I think the "Open" in OpenAI is about open access, not open
           | source. I'm really glad they are "democratic" in releasing
           | their products. I'd hate if I had to go through a "Talk to
           | our salespeople" form ik order to use them.
        
             | colordrops wrote:
             | No, that's not what they meant. Read the history of its
             | founding. They changed their charter after it was founded.
        
             | monsieurbanana wrote:
             | With that definition every single big software company is
             | open. Microsoft the most open of all, they don't care if
             | you pirate Windows as it brings them more market share.
        
             | throwawayapples wrote:
             | Then they wouldn't be nearly as good. You're training GPT
             | for them for free (or even paying them for the privilege.)
        
         | jacquesm wrote:
         | It might also be post/pre processing.
        
       | boringuser2 wrote:
       | Any type of additional explanation that isn't strictly necessary
       | is currently painful on the GPT-4 plan.
        
         | cubefox wrote:
         | Bing Chat (Sydney) has 20 turns per conversation and 200 turns
         | per day, free.
        
           | raylad wrote:
           | Which is the same as the paid ChatGPT plan when using GPT-4:
           | 25 turns every 3 hours.
           | 
           | Bing's free one is better in that sense in that you can use
           | all 200 over a shorter timespan.
        
       | chrispogeek wrote:
       | I think COT would still be useful for guiding the LLM to
       | customized context. Most of these examples refer to vanilla
       | arithmetic/logic reasoning COT.
        
       | leobg wrote:
       | I think the ideas is that the LLM cannot think internally. It's
       | output _is_ its thinking process. Especially with an auto
       | regressive architecture like GPT, where each output token becomes
       | part of the input.
       | 
       | I imagine it like handing the LLM a piece of scratch paper.
        
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