hprobes

Discover and causally validate hallucination-associated FFN neurons (H-Neurons) in transformer LLMs.

Based on arXiv:2512.01797.

Install

pip install hprobes
# or
uv add hprobes

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
from hprobes import HProbe

model = AutoModelForCausalLM.from_pretrained("google/gemma-3-4b-it", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-4b-it")

# samples: list of dicts with question, options, answer
probe = HProbe(model, tokenizer)
probe.fit(samples, options_key="choices", answer_key="answer")

print(probe.n_neurons_, probe.layer_distribution_)

results = probe.score()
print(f"AUROC {results['auroc']:.3f}  gap {results['auroc_gap']:+.3f}")

probe.causal_validate()

CLI

# Fit and score on an MCQ dataset
hprobes run --model google/gemma-3-4b-it --data dataset.jsonl --samples 500

# Transfer: score a saved probe on a different model
hprobes transfer --probe results/probe --model google/gemma-3-4b --data dataset.jsonl

# Fit from pre-generated responses with judge labels
hprobes responses --model google/gemma-3-4b-it --data responses.jsonl

Supported formats

Input files: .jsonl, .json, .parquet

Auto-detected dataset formats: mmlu, medqa, medmcqa. Any other format works by passing options_key and answer_key directly.

Key options

Save & load

probe.save("results/gemma_medqa")          # writes .json + .pkl
probe = HProbe.load("results/gemma_medqa", model, tokenizer)
probe.score_on(new_samples, options_key="choices", answer_key="answer")

Acknowledgements

This research is conducted in collaboration with the Great Ormond Street Hospital DRIVE Unit.

Contributors

• Huseyin Cavus — Core Contributor
• Pavithra Rajendran, PhD — Machine Learning Lead, GOSH DRIVE
• Sebin Sabu — Senior AI Scientist, GOSH DRIVE
• Jaskaran Singh Kawatra — ML Engineer, GOSH DRIVE
• Josh Spear, PhD — Postdoctoral Researcher, GOSH DRIVE