BioSum-CUH

A Biography Summarization Benchmark with Token-Level Correctness, Uncertainty, and Hallucination Annotations

BioSum-CUH is a benchmark for studying factual generation over biography contexts introduced in our paper, UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference. It combines biography-based question answering and structured summarization with token-aligned model predictions, final-layer attention activations, logit statistics, and three-way factuality annotations.

The release supports two complementary research settings:

1. Biography understanding: read a biography and recover one or more target attributes, including dates, places, education, employment, and other personal-profile fields.
2. Token-level reliability modeling: predict whether each generated token is correct/functional, hallucinated or contradictory, or part of an abstaining/unknown response.

The benchmark data and activation data are related but are not one-to-one. The benchmark contains 10,000 training examples, whereas the activation release contains 7,767 generation traces derived from 5,500 unique training examples. Repeated traces are expected because some source examples were collected under multiple generation or context-selection settings.

Dataset configurations

Configuration	Splits	Purpose
benchmark	train, validation, test	Biography contexts, questions, and reference answers
activations	train	Full token-level annotations, predictions, logits, and attention activations
activation-metadata	train	Lightweight projection for Dataset Viewer inspection and metadata analysis

benchmark is the default configuration. The full activation configuration is large because it preserves float32 tensors. Use streaming when only a subset is needed.

Benchmark splits

Split	Examples	Task design
train	10,000	Short synthetic biographies with one queried attribute
validation	120	Six attribute types, with 20 examples per type
test	100	Long-context, six-attribute biography summarization

The test split in this repository is the selected 100-example data_long split. It should not be confused with the separate 30-example data_long_long_common_short subset used by some internal evaluation runs.

Character-length statistics for the released biography text are shown below. These are Unicode character counts, not tokenizer-specific token counts.

Split	Minimum	Median	Mean	Maximum
train	986	1,017	1,020.2	1,157
validation	994	1,012	1,010.9	1,030
test	10,078	40,317	46,134.3	98,195

The training split covers 22 single-attribute question types:

marry date, job title, current city, email address, phone number, company, favorite color, user agent, credit card provider, currency used, catch phrase, street address, vehicle license plate, favorite file extension, domain name, cryptocurrency, timezone, isbn code, lucky number, hobby, marital status, and personality type.

The validation and test data focus on six biography attributes:

1. birth date;
2. birth place;
3. university;
4. major;
5. company;
6. work place.

Benchmark schema

Each row in the benchmark configuration contains:

Field	Type	Description
example_id	int64	Stable row identifier within the split
biography	string	Biography context presented to the model
name	string	Target person or entity name
question	string	Requested attribute or ordered attribute list
answer	string	Reference answer
prompt_template	string, nullable	Original prompt template when present in the source split

The test answers use an ordered six-field format. Models should preserve the requested order so that partial correctness can be evaluated per attribute.

Example:

{
  "example_id": 0,
  "name": "Emma Thompson",
  "question": "(1) birth date, (2) birth place, (3) university, (4) major, (5) company, (6) work place",
  "answer": "(1) 12/29/1880, (2) Tokyo, (3) Harvard University, (4) Computer Science, (5) Google, (6) San Francisco Bay Area",
  "biography": "...",
  "prompt_template": "Read the biography, and answer the question one by one..."
}

Activation and token-annotation data

The activations configuration contains 7,767 aligned generation records. The records contain predictions produced with Meta-Llama-3.1-8B-Instruct and the corresponding layer-31 attention outputs. All tensor columns are padded to 30 generated-token positions; length marks the valid prefix.

Backbone and model specificity

The released model-dependent artifacts were collected from Meta-Llama-3.1-8B-Instruct (Llama-3.1-8B-IT), using the final transformer layer (L31) of the 32-layer model. Each record contains a padded attention-output activation attn_act of shape [30, 4096], the top-1-minus-top-2 logit margin, the descending top-50 raw logits, and token-aligned decoded pieces produced by the Llama 3 Instruct tokenizer.

These artifacts are specific to this model, checkpoint, layer, and tokenizer and should not be treated as interchangeable with features from other models. To use BioSum-CUH with another model family, size, revision, layer, or tokenizer, reuse the biographies, questions, and reference answers from the benchmark configuration, but reconstruct all model-dependent records by:

1. generating predictions with the target model;
2. recording token-aligned activations from a documented layer;
3. recording logits and recomputing logit_margin and logit_topk;
4. recomputing length and prediction_tokens with the target tokenizer;
5. assigning token-level labels to the newly generated sequence; and
6. publishing a separate model-specific configuration documenting the model identifier, revision, tokenizer, layer, tensor shape, and dtype.

Padding or truncating another model's hidden states to 4096 dimensions does not reproduce the released Llama activation distribution and should not be presented as an equivalent reconstruction.

Field	Hugging Face feature	Description
activation_id	Value("int64")	Stable activation-record ID, from 0 to 7,766
source_train_index	Value("int64")	Corresponding row in the benchmark training split
source_occurrence	Value("int8")	Zero-based occurrence index for repeated collection of the same source row
collection_method	Value("string"), nullable	Collection mode when recoverable from provenance
name	Value("string")	Target name
question	Value("string")	Queried attribute
answer	Value("string")	Reference answer
prediction_tokens	List(Value("string"))	Tokenizer-decoded generated pieces in sequence order
labels	List(Value("int8"))	Unpadded token labels aligned one-to-one with prediction_tokens
length	Value("int32")	Number of valid generated-token positions, from 8 to 30
attn_act	Array2D((30, 4096), "float32")	Padded layer-31 attention-output activation
logit_margin	Array2D((30, 1), "float32")	Padded top-1-minus-top-2 logit margin
logit_topk	Array2D((30, 50), "float32")	Padded descending top-50 raw logits

prediction_tokens stores decoded token pieces rather than vocabulary IDs; leading spaces and punctuation are therefore meaningful and should be preserved.

The activation-metadata configuration contains the same identifiers, source mappings, predictions, labels, lengths, and logit metadata, but omits attn_act. It is intended for lightweight inspection rather than as an independent source of truth.

Token-label semantics

Label	Name	Meaning
0	hallucinated / incorrect	Wrong, contradictory, unsupported, or predicate-mismatched asserted content
1	correct / functional	Correct answer or name content, plus punctuation, spacing, and functional tokens that do not assert an incorrect fact
2	unknown / abstaining	Tokens expressing lack of knowledge, missing information, or a request for additional information

For the binary detector used in the accompanying research workflow, labels 0 and 2 are grouped as uncertain, while label 1 is treated as certain. The original three-way labels are preserved in this dataset so users can adopt different mappings.

These labels describe the semantic status of generated text. They are not direct measurements of calibrated epistemic or aleatoric uncertainty.

Label statistics

There are 122,453 valid labeled tokens:

Label	Tokens	Share
0	19,832	16.196%
1	101,924	83.235%
2	697	0.569%

The substantial class imbalance, especially the small number of label-2 tokens, should be considered when choosing metrics and sampling strategies.

Annotation and processing pipeline

1. Biography question-answer examples were constructed from synthetic target profiles. Training profiles use multilingual Faker locales to diversify names and attribute values, while prompts and questions are in English.
2. Reader-model outputs were collected under multiple generation/context settings. For each generated token, the pipeline recorded the decoded token, final-layer attention output, logits, and source-example metadata.
3. A GPT-OSS-120B-assisted labeling prompt assigned token-level labels according to the three-way rules above.
4. Records were padded to 30 token positions. Labels and predictions remain unpadded lists in the Hugging Face representation, and length preserves the valid tensor boundary.
5. The aligned artifacts were converted from PyTorch .pth/JSONL files into schema-defined, sharded Parquet files.

The following integrity checks were applied to the source artifacts:

• 7,767 records in every aligned artifact;
• exact len(labels) == len(prediction_tokens) == length for every record;
• tensor shapes of [30, 4096], [30, 1], and [30, 50];
• all 7,767 activation rows matched exactly one training row by (name, question, answer);
• 5,500 unique training rows represented, with at most three activation occurrences per source row;
• logit_margin == logit_topk[..., 0] - logit_topk[..., 1] bit-for-bit over all valid positions;
• zero padding after length in the source tensor artifacts.

Loading the data

Install the datasets package and load the benchmark configuration:

from datasets import load_dataset

dataset = load_dataset("Saria307/biosum-cuh", "benchmark")

train = dataset["train"]
validation = dataset["validation"]
test = dataset["test"]

print(train[0]["question"])
print(train[0]["answer"])

Stream the full activation configuration to avoid downloading the complete activation table before reading the first record:

from datasets import load_dataset
import numpy as np

stream = load_dataset(
    "Saria307/biosum-cuh",
    "activations",
    split="train",
    streaming=True,
)

row = next(iter(stream))
length = row["length"]

attn_act = np.asarray(row["attn_act"], dtype=np.float32)[:length]
logit_margin = np.asarray(row["logit_margin"], dtype=np.float32)[:length]
logit_topk = np.asarray(row["logit_topk"], dtype=np.float32)[:length]
labels = np.asarray(row["labels"], dtype=np.int64)

assert attn_act.shape == (length, 4096)
assert logit_margin.shape == (length, 1)
assert logit_topk.shape == (length, 50)
assert labels.shape == (length,)

To construct a new train/validation partition from the activation records, split by source_train_index, not by activation_id. Otherwise repeated collections of the same source example can leak across partitions.

Recommended evaluation

Biography task

For single-attribute examples, report normalized exact match and token-level F1. For the six-attribute test examples, also report per-attribute accuracy and the macro average across the six requested fields. Semantic variants of dates, locations, and organization names may require careful normalization or a documented judge model.

If an LLM judge is used, report the judge model and revision, full prompt, temperature, parsing/failure policy, and whether missing outputs receive zero. Judge-based scores should not be compared unless these settings are held fixed.

Token-level reliability task

For three-way prediction, report per-class precision, recall, F1, macro F1, and the confusion matrix. For binary uncertain-vs-certain prediction, explicitly state the label mapping; the accompanying workflow uses {0, 2} -> uncertain and {1} -> certain. Because the classes are imbalanced, accuracy alone is not sufficient.

Intended uses

BioSum-CUH is intended for research on:

• long-context biography summarization and attribute extraction;
• factuality and hallucination detection during generation;
• token-level uncertainty and abstention modeling;
• logit-based confidence baselines;
• activation-based probing and lightweight detector training;
• context-compression and KV-cache methods evaluated under a shared benchmark;
• analysis of correct, incorrect, and unknown generation behavior.

Out-of-scope uses

The dataset is not intended for:

• making claims about real people;
• identity verification, background checks, surveillance, or profiling;
• making medical, legal, financial, employment, credit, or other high-impact decisions;
• treating machine-generated labels as definitive human factuality judgments;
• comparing model uncertainty across tokenizers or architectures without accounting for representation and calibration differences.

Limitations

• Synthetic targets: Target profiles and many attributes are synthetic. Synthetic names and contact-like values can coincidentally resemble real people or real identifiers and must not be interpreted as verified facts.
• Long-context construction: Test contexts may include repeated filler and Wikipedia-derived distractor passages. Success may therefore reflect robustness to redundancy and distractors as well as biography understanding.
• Distribution shift: Training and validation biographies are approximately 1,000 characters, while test biographies are substantially longer. The split design intentionally contains a strong length and task-format shift.
• Machine-assisted labels: Token labels follow a particular prompt and labeling policy. The policy gives lexical matches and functional tokens priority, so token labels need not coincide with a sentence-level factuality judgment.
• Class imbalance: Unknown/abstaining tokens are rare.
• Repeated activation sources: The activation set contains repeated collections of some training examples. Random row-level splits can leak source information.
• Incomplete collection provenance: The merged activation artifacts do not reliably expose every original collection mode. Missing modes are left null rather than inferred from row order.
• Model specificity: The released activations and logits are specific to Meta-Llama-3.1-8B-Instruct, its tokenizer, layer indexing, and the collection pipeline. They should not be treated as model-independent features.
• Fixed output window: Tensor features preserve at most 30 generated-token positions per record.
• Judge sensitivity: Semantic evaluation can vary with answer normalization or judge-model choice.

Ethical and privacy considerations

The benchmark is designed for controlled research rather than representing real biographies. Nevertheless, generated names, addresses, phone-like strings, and other profile fields can accidentally overlap with real-world data. Users should avoid attempts to identify individuals and should not use the dataset for decisions about people.

Long test contexts may contain Wikipedia-derived passages about real people. Those passages can contain the biases, omissions, inaccuracies, or outdated information present in their source material. Source URLs should be retained for attribution and auditability.

Licensing and attribution

This is a mixed-provenance release:

• project-authored benchmark packaging, metadata, annotations, and conversion code are released under the Apache License 2.0;
• Wikipedia-derived text remains subject to the Creative Commons Attribution-ShareAlike license and any applicable source terms;
• generated predictions and model-derived activations may also be subject to the terms of the model used to produce them, including the Llama 3.1 license.

For this reason, the Dataset Card uses license: other rather than applying Apache-2.0 to every text span in the repository. Users are responsible for preserving attribution and complying with all applicable upstream terms.

Citation

If you use BioSum-CUH, cite the dataset repository:

@misc{zhou2026utaca,
      title={UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference}, 
      author={Lang Zhou and Shuxuan Li and Zhuohao Li and Shi Liu and Zhilin Zhao and Wei-Shi Zheng},
      year={2026},
      eprint={2603.18446},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.18446}, 
}

When a corresponding paper or archival release becomes available, add its citation alongside the repository citation above.

Contact

For questions, corrections, or removal requests, open a discussion at:

https://huggingface.co/datasets/Saria307/biosum-cuh/discussions

Or reach out to: zhoulang3@mail2.sysu.edu.cn