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Now the question is how can I, someone without a PhD in history but currently a PhD candidate in another discipline, use these tools to reliably interrogate top

Now the question is how can I, someone without a PhD in history but currently a PhD candidate in another discipline, use these tools to reliably interrogate topics of interest and produce at least a graduate level understanding of them? I know this is possible, but the further away I get from my core domains, the harder it is for me to use these tools in a way that doesn’t feel like too much blind faith (even if it works!)

kozikow 2y ago
> the harder it is for me to use these tools in a way that doesn’t feel like too much blind faith (even if it works!) I tend to ask multiple models and if they all give me roughly the same answer, then it's probably right.
- aquafox 2y ago
  > if they all give me roughly the same answer, then it's probably right. ... or they had a lot of overlapping training data in that area.
- energy123 2y ago
  Also keeping context short. Virtually all my cases of bad hallucinations with o1 have been when I've provided too much context or the conversation has been going on for too long. Starting a new chat fixes it. You can see this effect in the ARC-AGI evals, too much context impacts even o3(high).
- otabdeveloper4 2y ago
  Or maybe they were just trained on the same (incorrect) dataset.
aquafox 2y ago
You ask them for references and check yourself. They are good exploratory and hypothesis generating tools, but not more. Getting a sensible sounding answer should not be an excuse for you to confirm. Often, the devil is in the details.
simonw 2y ago
I think the trick here is to treat everything these models tell you as part of a larger information diet. Like if you have a friend who's very well-read and talkative but is also extremely confident and loves the sound of their own voice. You quickly learn to treat them as a source of probably-correct information, but only part of they way you learn any given topic. I do this with LLMs all the time: I'm constantly asking them clarifying questions about things, but I always assume that they might be making mistakes or feeding me convincing sounding half-truths or even full hallucinations. Being good at mixing together information from a variety of sources - of different levels of accuracy - is key to learning anything well.
- serviceberry 2y ago
  This strikes me as an odd claim. You don't hang around with a friend who makes things up because they somehow enhance your learning process. You hang around with them despite the fact they're annoyingly unreliable, presumably because you value their company for other reasons. Let's say you're trying to get a university degree, but having a professor who makes up 20% of what they say. Is that helping you "learn well"?
  - edgineer 2y ago
    Well that sounds like oral history, which is how all people used to learn. Strictly fact check everything you say seems like a modern invention.
  - ismailmaj 2y ago
    20% is a harsh figure but it could be a good entry point to figure out the unknown unknowns and go in depth once you have the relevant keywords using more reliable sources.
  - brandall10 2y ago
    You might want to read the academic criticisms of an influential pop history book written by an academic, such as Sapiens. And 20% is way overstated, esp for a SOTA model when it comes to verifiable facts.
sdesol 2y ago
I wrote a chat app built around mistrust for LLM responses. You can see an example here: https://beta.gitsense.com/?chat=ed907b02-4f03-477f-a5e4-ce9a03f33ce5 https://beta.gitsense.com/?chat=ed907b02-4f03-477f-a5e4-ce9a... If you click on the Evaluation links, you can see how you can use multiple LLMs to validate LLM response. The evaluation of the accurate response is interesting since Llama 3.3 was the most critical. https://beta.gitsense.com/?chat=fdfb053d-f0e2-4346-bdfc-73057b0ac5fe https://beta.gitsense.com/?chat=fdfb053d-f0e2-4346-bdfc-7305... At this point, you would ask Llama to explain why the response was not 100% which you can use to cross reference other LLMs or to do your own research.
AdieuToLogic 2y ago
> Now the question is how can I, someone without a PhD in history but currently a PhD candidate in another discipline, use these tools to reliably interrogate topics of interest and produce at least a graduate level understanding of them? You can't. Because LLM's are statistical generative text algorithms, dependent upon their training data set and subsequent reinforcement. Think Bayesian statistics. What you are asking for is "to reliably interrogate topics of interest", which is not what LLM's do. Concepts such as reliability are orthogonal to their purpose.
yannis 2y ago
I find them useful in summarizing State of the Art to get me going in a new topic, but then again so is Wikipedia. A useful side angle, if you using LaTeX, you can cut-and-paste references into ChatGPT and can turn them into Bibtex format with >80% success. For a PHD study though starting from textbooks, papers etc. it will be better, but can augment successfully, like any tool use it for what is best.

kozikow 2y ago

> the harder it is for me to use these tools in a way that doesn’t feel like too much blind faith (even if it works!) I tend to ask multiple models and if they all give me roughly the same answer, then it's probably right.

aquafox 2y ago
> if they all give me roughly the same answer, then it's probably right. ... or they had a lot of overlapping training data in that area.
energy123 2y ago
Also keeping context short. Virtually all my cases of bad hallucinations with o1 have been when I've provided too much context or the conversation has been going on for too long. Starting a new chat fixes it. You can see this effect in the ARC-AGI evals, too much context impacts even o3(high).
otabdeveloper4 2y ago
Or maybe they were just trained on the same (incorrect) dataset.

aquafox 2y ago

You ask them for references and check yourself. They are good exploratory and hypothesis generating tools, but not more. Getting a sensible sounding answer should not be an excuse for you to confirm. Often, the devil is in the details.

simonw 2y ago

I think the trick here is to treat everything these models tell you as part of a larger information diet. Like if you have a friend who's very well-read and talkative but is also extremely confident and loves the sound of their own voice. You quickly learn to treat them as a source of probably-correct information, but only part of they way you learn any given topic. I do this with LLMs all the time: I'm constantly asking them clarifying questions about things, but I always assume that they might be making mistakes or feeding me convincing sounding half-truths or even full hallucinations. Being good at mixing together information from a variety of sources - of different levels of accuracy - is key to learning anything well.

serviceberry 2y ago
This strikes me as an odd claim. You don't hang around with a friend who makes things up because they somehow enhance your learning process. You hang around with them despite the fact they're annoyingly unreliable, presumably because you value their company for other reasons. Let's say you're trying to get a university degree, but having a professor who makes up 20% of what they say. Is that helping you "learn well"?
- edgineer 2y ago
  Well that sounds like oral history, which is how all people used to learn. Strictly fact check everything you say seems like a modern invention.
- ismailmaj 2y ago
  20% is a harsh figure but it could be a good entry point to figure out the unknown unknowns and go in depth once you have the relevant keywords using more reliable sources.
- brandall10 2y ago
  You might want to read the academic criticisms of an influential pop history book written by an academic, such as Sapiens. And 20% is way overstated, esp for a SOTA model when it comes to verifiable facts.

sdesol 2y ago

I wrote a chat app built around mistrust for LLM responses. You can see an example here: https://beta.gitsense.com/?chat=ed907b02-4f03-477f-a5e4-ce9a03f33ce5 https://beta.gitsense.com/?chat=ed907b02-4f03-477f-a5e4-ce9a... If you click on the Evaluation links, you can see how you can use multiple LLMs to validate LLM response. The evaluation of the accurate response is interesting since Llama 3.3 was the most critical. https://beta.gitsense.com/?chat=fdfb053d-f0e2-4346-bdfc-73057b0ac5fe https://beta.gitsense.com/?chat=fdfb053d-f0e2-4346-bdfc-7305... At this point, you would ask Llama to explain why the response was not 100% which you can use to cross reference other LLMs or to do your own research.

AdieuToLogic 2y ago

> Now the question is how can I, someone without a PhD in history but currently a PhD candidate in another discipline, use these tools to reliably interrogate topics of interest and produce at least a graduate level understanding of them? You can't. Because LLM's are statistical generative text algorithms, dependent upon their training data set and subsequent reinforcement. Think Bayesian statistics. What you are asking for is "to reliably interrogate topics of interest", which is not what LLM's do. Concepts such as reliability are orthogonal to their purpose.

yannis 2y ago

I find them useful in summarizing State of the Art to get me going in a new topic, but then again so is Wikipedia. A useful side angle, if you using LaTeX, you can cut-and-paste references into ChatGPT and can turn them into Bibtex format with >80% success. For a PHD study though starting from textbooks, papers etc. it will be better, but can augment successfully, like any tool use it for what is best.