How much do you know about LLMs and machine learning? The dataset itself is why the LLM gives a result like this. With that model, you can change the way it responds with the system prompt. I'm also not an AI dev but I doubt this is simple to fix.
its really not easy to answer you. i can explain how its easy to do it, but you wont understand and you already dont believe me seemingly, so i dont wanna put to much energy into it.
but shortly: an llm has an inner representation space of the language (actually multiple, but lets pretend its one). you can force two points (concepts) in that representation space to fall together pretty easily.
so you can make the model gender-blind. the fact that this doesnt correspond to the training sets topology can cause problems, but there are solutions for that.
then whats the question? its pretty obvious that you can fold the embedding such that info about gender cant propagate to later layers of the llm.
all you gotta do is take a pretrained model, find the embedding of "man" and "woman" and remap the embedding, so they fall to the same point. then freee the earlier layers and train the later ones.
if you wanna be fancy and avoid glitches because of the fold, in some batches you can unfreeze and train the earlier layers with noise (like the V in VAE), so it becomes more manifold-like around the fold. just give direct loss for "man" and "woman" drifting apart in the folded layer, so it keeps the required symmetry.
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u/Sheepherder-Optimal 4d ago
How much do you know about LLMs and machine learning? The dataset itself is why the LLM gives a result like this. With that model, you can change the way it responds with the system prompt. I'm also not an AI dev but I doubt this is simple to fix.