undefined

points

[-]

Obviously there are advantages to not having to do work yourself.

But for a benchmark with the goal of picking a model to replace a human on some task? I really think the human should judge which is best.

I haven’t gotten very far yet but I had an idea for a personalized benchmark tool that walks through your git history and helps you craft prompts for tasks that bugs or features already implemented by hand so you can compare how different LLMs would do it.

by rhdunn12 hours ago|

prev|

[-]

LLMs grading the answers is relying on the LLM knowing the answer and not just hallucinating it. You also have issues if/when the model refuses to answer, or if it gets stuck in a loop (e.g. if running locally with a heavily quantized model).

I'm investigating/experimenting with using traditional NLP (stanza, spaCy, etc.) to try and grade the responses according to different metrics (is the response in first/second/third person?, is it written as poetry, prose, or drama? etc.). I'm also thinking about using information extraction and synonym detection to handle data queries and the like.

by charcircuit12 hours ago|

parent|

[-]

>LLMs grading the answers is relying on the LLM knowing the answer and not just hallucinating it. You also have issues if/when the model refuses to answer, or if it gets stuck in a loop (e.g. if running locally with a heavily quantized model).

And LLMs have gotten good at handling these issues. There is asymmetric difficulty in generating a solution and verifying it correct. And overtime LLMs are getting better and better which allows training on synthetic data to make it better.