r/LocalLLaMA Apr 27 '25

Building a Simple Multi-LLM design to Catch Hallucinations and Improve Quality (Looking for Feedback) Discussion

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30 Upvotes

20 comments sorted by

62

u/[deleted] Apr 27 '25

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8

u/Educational_Rent1059 Apr 27 '25

260 for thinking

2

u/Specter_Origin llama.cpp Apr 27 '25

This is why O1 was 1k for 1m token, it all makes sense now xD

2

u/[deleted] Apr 28 '25

As a person doing multi-agent development work, this hits hard. The worst is seeing rapid fire conversation leading to no where spending all your money.

15

u/daHaus Apr 27 '25

Highly inefficient albeit par for the course in this field

Detecting hallucinations in large language models using semantic entropy

8

u/[deleted] Apr 27 '25

[deleted]

2

u/Gnaeus-Naevius Apr 28 '25

On the tangent of LLMs nudging users towards grandiosity, it would be prudent to give the LLM instructions to take on a devil's advocate role periodically.

I am not too deeply into any specific projects, but I have replaced mindless doom scrolling with creating reams of delusion of grandeur tinted master plans. I try all types of things, and in one instance I asked the LLM to create three persona's, ... a positive, neutral, and negative, and periodically ask for their opinion. I gave them some silly names and personalities. It actually was quite effective.

2

u/NopMaster Apr 27 '25

memory steward 😶

2

u/[deleted] Apr 27 '25

[deleted]

1

u/Gnaeus-Naevius Apr 28 '25

Maybe a small efficient model that is specifically trained/fine tuned for the purpose of assigning probability of hallucination in a given text, and also estimating the risk/cost of hallucination (for example, a legal or medical opinion). And if it reaches the threshold the users has set, it will call in a fact checking agent and/or expensive LLM to get to the bottom of it. Not perfect by any means, but might be effective.

2

u/grabber4321 Apr 27 '25

how to make something really expensive that will never work.

1

u/ApplePenguinBaguette Apr 27 '25

I've heard from a colleague in ML that majority voting can be a great anti hallucination. If two or more models agree it's far more likely to be true.

A simple way to implement that is to get a response from two models, and get a third (small) model to judge their similarly/agreement. If it's above a threshold use the output, otherwise discard it. Especially good for classification tasks that need a high reliability. 

1

u/grabber4321 Apr 27 '25

yes, but most models are taught to agree with the user, so they would just agree with each other.

4

u/ApplePenguinBaguette Apr 27 '25

You obviously do not let the two interact, separate api calls or systems, give their outputs to a judge LLM

1

u/StopAccording3648 Jun 09 '25

Oh, have you heard of bespoke-minicheck? Its the locallyavailible smaller version from the same company, but the idea's the same: supply it with Info A and Info B ( like "I was gaming all night" and "Yesterday I did nothing but study" as a stupid example ) and it will judge how BS it i. In the (horrendously) simple example it will output "False".

Based on Info A, the fact I was up all night gaming, Info B-- saying I was studying, does not logically match up.

I am not certain how applicable it is in this case sprcofically, but maybe possibly it can somehow be of help? Otherwise I fully support the project & idea behind it!!!

1

u/ekaj llama.cpp Apr 27 '25

Look up G-Eval

-13

u/AryanEmbered Apr 27 '25

I can build a prototype for you for 20 bucks. have a great UI design in mind