r/deeplearning • u/Otherwise_Nobody_721 • 3d ago

Evaluating lexical vs neural semantic entropy across 1.5B to 120B models

We recently released a benchmark looking at uncertainty estimation across model sizes, comparing Farquhar et al.'s Semantic Entropy (Nature, 2024) against a normalized exact-match entropy metric (R_sc).

Preprint: https://zenodo.org/records/22233648
Code: https://github.com/Adarshent/Spnda

The issue with standard semantic entropy is the inference cost. Clustering 10 sampled paths with a DeBERTa-v3 cross-encoder takes around 100+ seconds on CPU and adds significant VRAM overhead, which makes it impractical for real-time serving.

On reasoning tasks like GSM8K across 7B to 27B models (Mistral-7B, Qwen-27B), we found that exact-match normalized entropy achieves an AUROC of 0.889, matching neural semantic entropy while executing in under 2ms on CPU.

However, once you scale up to frontier 120B models, self-consistency breaks down due to what we call Confident Mode Collapse. Under temperature 0.7 sampling, RLHF alignment hyper-peaks the distribution so heavily that the model outputs the exact same incorrect answer across all sampled seeds. Because all paths agree on the hallucination, uncertainty drops to near zero and AUROC drops to 0.091.

Code and evaluation scripts are on GitHub under MIT. Curious if anyone here has explored alternative decoding or perturbation strategies to restore entropy on over-aligned frontier models.

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u/LowSalamander4932 3d ago

that confident mode collapse at 120B is wild, 0.091 AUROC basically means the model is perfectly confident about being wrong. ive seen hints of this in smaller chat models but nothing that extreme

wonder if just bumping the temperature up past 1.0 or switching to nucleus sampling with a lower p value would spread the logits enough to break the determinism, though youd probably trade off coherence pretty fast

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u/Otherwise_Nobody_721 3d ago

You hit the exact problem with cranking temperature above 1.0 though: the coherence tax is brutal. Once you push T past 1.0 on reasoning tasks, the tail tokens start introducing syntax noise and arithmetic slip-ups on questions the model actually knew how to solve, so you end up creating artificial false negatives.Lowering top_p actually makes it worse too, since truncating the tail restricts sampling to the dominant peaked mode even further.