README.md

metadata

license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
  - hallucination-detection
  - token-classification
  - qwen3
language:
  - en

TokenHD-8B (multi-domain)

TokenHD is a token-level hallucination detector trained on top of Qwen/Qwen3-8B using the TokenHD pipeline. It assigns a hallucination probability to each token in an LLM-generated response, enabling fine-grained localization of errors without requiring predefined step segmentation.

Paper: arxiv.org/abs/2605.12384
Code: github.com/rmin2000/TokenHD
Training Data: mr233/TokenHD-training-data

---

Model Details

Property	Value
Base model	Qwen/Qwen3-8B
Architecture	AutoModelForTokenClassification (num_labels=1)
Training domain	Mathematics and code generation (multi-domain training)
Output	Per-token hallucination probability (sigmoid of logits)

---

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_id = "mr233/TokenHD-8B-Mix"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id, num_labels=1)
model.eval()

problem = "What is the capital of France?"
response = "The capital of France is London."

messages = [
    {"role": "user", "content": problem},
    {"role": "assistant", "content": response},
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=False)[:-2]
input_tensor = torch.tensor(input_ids).unsqueeze(0)

with torch.no_grad():
    logits = model(input_ids=input_tensor).logits  # shape: (1, seq_len, 1)

# scores for response tokens only
response_ids = tokenizer.encode(response, add_special_tokens=False)
scores = torch.sigmoid(logits.squeeze(-1).squeeze(0))[-len(response_ids):]
# scores[i] is the hallucination probability for the i-th response token

---

Evaluation

Use the TokenHD eval dataset to compute S_incor (token F1 on hallucinated samples) and S_cor (recall on hallucination-free samples):

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
import numpy as np

def hard_f1(y_true, y_pred):
    if max(y_true) == 0:
        y_true, y_pred = 1 - y_true, 1 - y_pred
    tp = np.sum((y_pred == 1) & (y_true == 1))
    fp = np.sum((y_pred == 1) & (y_true == 0))
    fn = np.sum((y_pred == 0) & (y_true == 1))
    precision = tp / (tp + fp + 1e-7)
    recall    = tp / (tp + fn + 1e-7)
    f1        = 2 * precision * recall / (precision + recall + 1e-7)
    return precision, recall, f1

model_id = "mr233/TokenHD-8B-Mix"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(
    model_id, num_labels=1, torch_dtype=torch.bfloat16, device_map="auto"
)
model.eval()

benchmarks = [
    "tokenhd_eval_math_500",
    "tokenhd_eval_math_aime",
    "tokenhd_eval_math_gpqa",
    "tokenhd_eval_math_fin_qa",
    "tokenhd_eval_math_olym",
    "tokenhd_eval_math_olym_phy",
    "tokenhd_eval_code_codeelo",
    "tokenhd_eval_code_live_code_lite",
]

for bench in benchmarks:
    dataset = load_dataset("mr233/TokenHD-eval-data",
                           data_files=f"{bench}.jsonl", split="train")
    f1_incor, f1_cor = [], []
    for item in dataset:
        token_weights_gt = np.array(item["token_weights"], dtype=np.float32)
        gt_hard = (token_weights_gt > 0.5).astype(np.float32)

        messages = [{"role": "user",      "content": item["problem"]},
                    {"role": "assistant", "content": item["raw_answer"]}]
        input_ids = tokenizer.apply_chat_template(
            messages, tokenize=True, add_generation_prompt=False)[:-2]
        input_tensor = torch.tensor(input_ids, device=model.device).unsqueeze(0)

        with torch.no_grad():
            logits = model(input_ids=input_tensor).logits
        scores = torch.sigmoid(logits.squeeze(-1).squeeze(0))[-len(gt_hard):]
        pred_hard = (scores.float().cpu().numpy() > 0.5).astype(np.float32)

        _, _, f1 = hard_f1(gt_hard, pred_hard)
        if item["correctness"] == -1:
            f1_incor.append(f1)
        else:
            f1_cor.append(f1)

    s_incor = np.mean(f1_incor) * 100 if f1_incor else float("nan")
    s_cor   = np.mean(f1_cor)   * 100 if f1_cor   else float("nan")
    print(f"{bench:<44s}  S_incor={s_incor:.2f}  S_cor={s_cor:.2f}")