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Whatever happened to feature extraction/selection/engineering and then training a model on your data for a specific purpose? Don't get me wrong, LLMs are incre

Whatever happened to feature extraction/selection/engineering and then training a model on your data for a specific purpose? Don't get me wrong, LLMs are incredible at what they do, but prompting one with a job description + a number of CVs and asking it to select the best candidate is not it.

jsemrau 1y ago
If the question is to understand the default training/bias then this approach does make sense, though. For most people LLMs are black box models and this is one way to understand their bias. That said, I'd argue that most LLMs are neither deterministic not reliable in their "decision" making unless prompts and context are specifically prepared.
- HappMacDonald 1y ago
  I'm not sure what you mean by "deterministic". You can set the sampling temperature to zero (greedy sampling), or alternately use an ultra simple seeded PRNG to break up the ties in anything other than greedy sampling. LLM inference outputs a list of probabilities for next token to select on each round. A majority of the time (especially when following semantic boilerplate like quoting an idiom or obeying a punctuation rule) one token is rated 10x or more likely than every other token combined, making that the obvious natural pick. But every now and then the LLM will rate 2 or more tokens as close to equally valid options (such as asking it to "tell a story" and it gets to the hero's name.. who really cares which name is chosen? The important part is sticking to whatever you select!) So for basically the same reason as D&D, the algorithm designers added a dice roll as tie-breaker stage to just pick one of the equally valid options in a manner every stakeholder can agree is fair and get on with life. Since that's literally the only part of the algorithm where any randomness occurs aside from "unpredictable user at keyboard", and it can be easily altered to remove every trace of unpredictability (at the cost of only user-perceived stuffiness and lack of creativity.. and increased likelihood of falling into repetition loops when one chooses greedy sampling in particular to bypass it) I am at a loss why you would describe LLMs as "not deterministic".
  - jsemrau 1y ago
    When I did my research on reasoning strategic games in 4x4 tic tac toe boards, LLMs with given nominal parameters and low temperature would still show variance in their assessment of the situation.
    - Sohcahtoa82 1y ago
      > low temperature "low" is the key word. If it's anything other than 0, it becomes non-deterministic. If you use a temperature of 0, then the output of an LLM will be completely deterministic. Any given input would have the exact same output every time.
mathgeek 1y ago
It’s much easier and cheaper for the average person today to build a product on top of an existing LLM than to train their own model. Most “AI companies” are doing that.
- ldng 1y ago
  You are conflating Neural Model with Large Langage Model There are a lot more models than just LLM. Small specialized model are not necessarily costly to build and can be as (if not more) efficient and cheaper; both in term of training and inference.
  - hobs 1y ago
    Yes, but most of those "AI Companies" are actually "AI Slop" companies and have little to no Machine Learning experience of any kind.
  - mathgeek 1y ago
    I’m not implying what you inferred. I am only referring to LLMs in response to GP. Another way to put it is most people building AI products are just using the existing LLMs instead of creating new models. It’s a gold rush akin to early mobile apps.
empath75 1y ago
I agree. LLM's can make convincing arguments for almost anything. For something like this, what would be more useful is having it go through all of them individually and generate a _brief_ report about whether and how the resume matches the job description, along with an short argument both _for_ and _against_ advancing the resume, and then let a real recruiter flip through those and make the decision. One advantage that LLM's have over recruiters, especially for technical stuff is that they "know" what all the jargon means the relationships between various technologies and skill sets, so they can call out stuff that a simple keyword search might miss. Really, if you spend any time thinking about it, you can probably think of 100 ways that you can usefully apply LLMs to recruiting that don't involve "making decisions".

jsemrau 1y ago

If the question is to understand the default training/bias then this approach does make sense, though. For most people LLMs are black box models and this is one way to understand their bias. That said, I'd argue that most LLMs are neither deterministic not reliable in their "decision" making unless prompts and context are specifically prepared.

HappMacDonald 1y ago
I'm not sure what you mean by "deterministic". You can set the sampling temperature to zero (greedy sampling), or alternately use an ultra simple seeded PRNG to break up the ties in anything other than greedy sampling. LLM inference outputs a list of probabilities for next token to select on each round. A majority of the time (especially when following semantic boilerplate like quoting an idiom or obeying a punctuation rule) one token is rated 10x or more likely than every other token combined, making that the obvious natural pick. But every now and then the LLM will rate 2 or more tokens as close to equally valid options (such as asking it to "tell a story" and it gets to the hero's name.. who really cares which name is chosen? The important part is sticking to whatever you select!) So for basically the same reason as D&D, the algorithm designers added a dice roll as tie-breaker stage to just pick one of the equally valid options in a manner every stakeholder can agree is fair and get on with life. Since that's literally the only part of the algorithm where any randomness occurs aside from "unpredictable user at keyboard", and it can be easily altered to remove every trace of unpredictability (at the cost of only user-perceived stuffiness and lack of creativity.. and increased likelihood of falling into repetition loops when one chooses greedy sampling in particular to bypass it) I am at a loss why you would describe LLMs as "not deterministic".
- jsemrau 1y ago
  When I did my research on reasoning strategic games in 4x4 tic tac toe boards, LLMs with given nominal parameters and low temperature would still show variance in their assessment of the situation.
  - Sohcahtoa82 1y ago
    > low temperature "low" is the key word. If it's anything other than 0, it becomes non-deterministic. If you use a temperature of 0, then the output of an LLM will be completely deterministic. Any given input would have the exact same output every time.

mathgeek 1y ago

It’s much easier and cheaper for the average person today to build a product on top of an existing LLM than to train their own model. Most “AI companies” are doing that.

ldng 1y ago
You are conflating Neural Model with Large Langage Model There are a lot more models than just LLM. Small specialized model are not necessarily costly to build and can be as (if not more) efficient and cheaper; both in term of training and inference.
- hobs 1y ago
  Yes, but most of those "AI Companies" are actually "AI Slop" companies and have little to no Machine Learning experience of any kind.
- mathgeek 1y ago
  I’m not implying what you inferred. I am only referring to LLMs in response to GP. Another way to put it is most people building AI products are just using the existing LLMs instead of creating new models. It’s a gold rush akin to early mobile apps.

empath75 1y ago

I agree. LLM's can make convincing arguments for almost anything. For something like this, what would be more useful is having it go through all of them individually and generate a _brief_ report about whether and how the resume matches the job description, along with an short argument both _for_ and _against_ advancing the resume, and then let a real recruiter flip through those and make the decision. One advantage that LLM's have over recruiters, especially for technical stuff is that they "know" what all the jargon means the relationships between various technologies and skill sets, so they can call out stuff that a simple keyword search might miss. Really, if you spend any time thinking about it, you can probably think of 100 ways that you can usefully apply LLMs to recruiting that don't involve "making decisions".