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It's really poor work. There are two conclusions I took from scanning through this and trying to reproduce a few of the reported failures. 1. The author is ba

by dereg 3y ago

It's really poor work. There are two conclusions I took from scanning through this and trying to reproduce a few of the reported failures. 1. The author is bad at prompting. There are many ways to reduce hallucinations and provoke better thinking paths for the model. 2. The author is using ChatGPT's GPT-4, leading him to conflate "GPT-4" with "ChatGPT". While you can consider this a shared failure with OpenAI, due to OpenAI's poor communication, anybody doing serious work evaluating these models would know that the first thing you need to do is use the API and pin the model version. In the author's case, he should have used gpt-4-0314 or gpt-4-0613. What I suspect he did is that he just used ChatGPT's GPT-4, and likely the default model at that. (Nobody should ever use the Default model. It's their most heavily performance optimized model and performs worse on reasoning tasks than the Plugins model, even on within-context-size tasks.) There are huge problems with that, because OpenAI has done both a ton of fine tuning and performance optimization continuously on the default ChatGPT model over time that its performance has ranged anywhere from "I'm pretty sure this is gpt-3.5" to "whoa, this is damn good" (the latter being mostly the model at launch, which was probably the same as gpt-4-0314). If the author has been working seriously at evaluating models, specifying the model is the first thing he'd do. Perhaps he should explain his reasoning.

whimsicalism 3y ago
Uh, what? Do you have any source that GPT4 on web is not gpt-4-0613? Because I do not think that is true, my guess is you just have different settings for the decoder
- dereg 3y ago
  GPT-4 in ChatGPT has changed so much and so frequently. I've spent an inordinate time using both, side by side, and it has mostly failed to replicate the performance of their API offerings. While I don't have examples on hand right now, you can also look for yourself. Just look at the network requests in the web inspector. Here's the description of the Default ChatGPT GPT-4 model: { "slug": "gpt-4", "max_tokens": 4095, "title": "GPT-4", "description": "Our most capable model, great for tasks that require creativity and advanced reasoning.", "tags": [ "gpt4" ], "capabilities": {}, "product_features": {} } Note the context size is 4095. Their model has been heavily optimized for speed and, presumably, cost.
  - whimsicalism 3y ago
    I do not see what that description shows. From the OAI API, gpt-4 seems to be an alias for the most recent model two weeks after it is released. There has not been a release since 0613. https://platform.openai.com/docs/models/gpt-4 https://platform.openai.com/docs/models/gpt-4 e: from your edit, "Note the context size is 4095. Their model has been heavily optimized for speed and, presumably, cost." No, they are restricting context size to make inference on the chat interface cheaper but that does not mean it is a different model.
    - dereg 3y ago
      Keep in mind that the information in the link you provided only applies to the developer models, which are only accessible via API and OpenAI Playground. OpenAI's developer relations person confirmed it a little whiles back: > [..] I made the comment elsewhere, but the models are always changing in ChatGPT. https://twitter.com/OfficialLoganK/status/1664477070263570432 https://twitter.com/OfficialLoganK/status/166447707026357043...
  - tarvaina 3y ago
    I would guess max_tokens refers to the number of generated tokens, not the size of the context window. That at least is the meaning of max_tokens in the ChatGPT API. https://platform.openai.com/docs/api-reference/chat/create#chat/create-max_tokens https://platform.openai.com/docs/api-reference/chat/create#c...
- furyofantares 3y ago
  Some time ago when people started reporting degradation, Logan.GPT (Developer Relations @OpenAI) clarified that the API models don't change. This caused a lot of confusion because people thought that was a claim that ChatGPT doesn't change. He then further clarified that "the models are changing all the time in ChatGPT". https://nitter.net/OfficialLoganK/status/1664476604658069511 https://nitter.net/OfficialLoganK/status/1664476604658069511
brabel 3y ago
> Perhaps he should explain his reasoning. Ha! To evaluate an AI's reasoning, you need to be better at reasoning than the AI, which is becoming very difficult as AI improves.
- dragonwriter 3y ago
  > Ha! To evaluate an AI’s reasoning, you need to be better at reasoning than the AI No, you don’t. You do, OTOH, have to have well-defined criteria for what constitutes “reasoning”.
- admax88qqq 3y ago
  More like you to evaluate an AI's reasoning, you need to be good enough at reasoning yourself to ensure you're actually evaluating the AI you intended to evaluate.
- deleted 3y ago
  [deleted]
password54321 3y ago
> 1. The author is bad at prompting. There are many ways to reduce hallucinations and provoke better thinking paths for the model. Does "Provoke better thinking paths" mean re-rolling the dice until you find some hack specific to chatGPT that 'just works' or is there something more rigorous behind this?
- Kareem71 3y ago
  Seriously this is the part I dont understand about people parroting "prompt engineering" . Isn't it really just throwing random things at a non deterministic black box and hoping for the best?
  - lsaferite 3y ago
    I find it's more like that silly experiment where you have to make a sandwich exactly as a kid (or adult) writes the instructions. You _think_ you have a good set of instructions and then you get peanut butter on the outside. So, you revisit the instructions to be clearer about what you want done. That's how I see prompt engineering. In that case, you are simply learning how the model tends to follow instructions and crafting a prompt around that. Not so much random, more purposeful.
    - FormerBandmate 3y ago
      That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want. It’s Clever Hans on steroids
      - dingosity 3y ago
        As always, it's unclear on which side of the keyboard the intelligence lies.
      - dragonwriter 3y ago
        > That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want. If its to get the model to present a fixed answer, sure. If its to get a model to do a better job at solving general classes of problems (such as when what you are optimizing is the built-in prompt in a ReAct/Reflexion implementation, not the prompt for a specific problem), that's, at a minimum, different from Clever Hans, even if its not “reasoning” (which is ill-defined).
flangola7 3y ago
>Nobody should ever use the Default model. It's their most heavily performance optimized model and performs worse on reasoning tasks than the Plugins model, even on within-context-size tasks. I wish to explore this. My experience is your reverse, default is smart and almost never hallucinates, but I have sent the plugin or web search model to URLs asking it produce a summary and witnessed it misunderstand nuanced content and at times hallucinate from whole cloth, generating answers about a completely unrelated topic.
YeGoblynQueenne 3y ago
>> The author is using ChatGPT's GPT-4, leading him to conflate "GPT-4" with "ChatGPT". For the last three years or so every time someone reports negative results with an LLM, someone on HN will say the other person must be using the older model and they would get better results if they used the newest model. Then, when the newest model becomes common and people start posting more negative results with it, someone will post on HN to say "It's still early days, give it time, the models will improve". This is such massive shifting of the goalposts that I can almost visualise the scene: a football stadium, the crowd jeering, two teams moving their goalposts around the pitch while the referee is jumping up and down blowing his whistle red in the face, in risk of swallowing the pea. And nobody is playing ball. * football = soccer.

by dereg 3y ago

whimsicalism 3y ago

Uh, what? Do you have any source that GPT4 on web is not gpt-4-0613? Because I do not think that is true, my guess is you just have different settings for the decoder

dereg 3y ago
GPT-4 in ChatGPT has changed so much and so frequently. I've spent an inordinate time using both, side by side, and it has mostly failed to replicate the performance of their API offerings. While I don't have examples on hand right now, you can also look for yourself. Just look at the network requests in the web inspector. Here's the description of the Default ChatGPT GPT-4 model: { "slug": "gpt-4", "max_tokens": 4095, "title": "GPT-4", "description": "Our most capable model, great for tasks that require creativity and advanced reasoning.", "tags": [ "gpt4" ], "capabilities": {}, "product_features": {} } Note the context size is 4095. Their model has been heavily optimized for speed and, presumably, cost.
- whimsicalism 3y ago
  I do not see what that description shows. From the OAI API, gpt-4 seems to be an alias for the most recent model two weeks after it is released. There has not been a release since 0613. https://platform.openai.com/docs/models/gpt-4 https://platform.openai.com/docs/models/gpt-4 e: from your edit, "Note the context size is 4095. Their model has been heavily optimized for speed and, presumably, cost." No, they are restricting context size to make inference on the chat interface cheaper but that does not mean it is a different model.
  - dereg 3y ago
    Keep in mind that the information in the link you provided only applies to the developer models, which are only accessible via API and OpenAI Playground. OpenAI's developer relations person confirmed it a little whiles back: > [..] I made the comment elsewhere, but the models are always changing in ChatGPT. https://twitter.com/OfficialLoganK/status/1664477070263570432 https://twitter.com/OfficialLoganK/status/166447707026357043...
- tarvaina 3y ago
  I would guess max_tokens refers to the number of generated tokens, not the size of the context window. That at least is the meaning of max_tokens in the ChatGPT API. https://platform.openai.com/docs/api-reference/chat/create#chat/create-max_tokens https://platform.openai.com/docs/api-reference/chat/create#c...
furyofantares 3y ago
Some time ago when people started reporting degradation, Logan.GPT (Developer Relations @OpenAI) clarified that the API models don't change. This caused a lot of confusion because people thought that was a claim that ChatGPT doesn't change. He then further clarified that "the models are changing all the time in ChatGPT". https://nitter.net/OfficialLoganK/status/1664476604658069511 https://nitter.net/OfficialLoganK/status/1664476604658069511

brabel 3y ago

> Perhaps he should explain his reasoning. Ha! To evaluate an AI's reasoning, you need to be better at reasoning than the AI, which is becoming very difficult as AI improves.

dragonwriter 3y ago
> Ha! To evaluate an AI’s reasoning, you need to be better at reasoning than the AI No, you don’t. You do, OTOH, have to have well-defined criteria for what constitutes “reasoning”.
admax88qqq 3y ago
More like you to evaluate an AI's reasoning, you need to be good enough at reasoning yourself to ensure you're actually evaluating the AI you intended to evaluate.
deleted 3y ago
[deleted]

password54321 3y ago

> 1. The author is bad at prompting. There are many ways to reduce hallucinations and provoke better thinking paths for the model. Does "Provoke better thinking paths" mean re-rolling the dice until you find some hack specific to chatGPT that 'just works' or is there something more rigorous behind this?

Kareem71 3y ago
Seriously this is the part I dont understand about people parroting "prompt engineering" . Isn't it really just throwing random things at a non deterministic black box and hoping for the best?
- lsaferite 3y ago
  I find it's more like that silly experiment where you have to make a sandwich exactly as a kid (or adult) writes the instructions. You _think_ you have a good set of instructions and then you get peanut butter on the outside. So, you revisit the instructions to be clearer about what you want done. That's how I see prompt engineering. In that case, you are simply learning how the model tends to follow instructions and crafting a prompt around that. Not so much random, more purposeful.
  - FormerBandmate 3y ago
    That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want. It’s Clever Hans on steroids
    - dingosity 3y ago
      As always, it's unclear on which side of the keyboard the intelligence lies.
    - dragonwriter 3y ago
      > That isn’t the model reasoning. That’s you figuring out exactly what parameters you need to use to make the model give the result you want. If its to get the model to present a fixed answer, sure. If its to get a model to do a better job at solving general classes of problems (such as when what you are optimizing is the built-in prompt in a ReAct/Reflexion implementation, not the prompt for a specific problem), that's, at a minimum, different from Clever Hans, even if its not “reasoning” (which is ill-defined).

flangola7 3y ago

>Nobody should ever use the Default model. It's their most heavily performance optimized model and performs worse on reasoning tasks than the Plugins model, even on within-context-size tasks. I wish to explore this. My experience is your reverse, default is smart and almost never hallucinates, but I have sent the plugin or web search model to URLs asking it produce a summary and witnessed it misunderstand nuanced content and at times hallucinate from whole cloth, generating answers about a completely unrelated topic.

YeGoblynQueenne 3y ago

>> The author is using ChatGPT's GPT-4, leading him to conflate "GPT-4" with "ChatGPT". For the last three years or so every time someone reports negative results with an LLM, someone on HN will say the other person must be using the older model and they would get better results if they used the newest model. Then, when the newest model becomes common and people start posting more negative results with it, someone will post on HN to say "It's still early days, give it time, the models will improve". This is such massive shifting of the goalposts that I can almost visualise the scene: a football stadium, the crowd jeering, two teams moving their goalposts around the pitch while the referee is jumping up and down blowing his whistle red in the face, in risk of swallowing the pea. And nobody is playing ball. * football = soccer.