README.md

metadata

pretty_name: GI-TraceBench
license: apache-2.0
language:
  - en
task_categories:
  - question-answering
  - text-generation
size_categories:
  - 10K<n<100K
tags:
  - science
  - biomedical
  - provenance
  - evidence-grounded-reasoning
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: data/train.parquet
      - split: test
        path: data/test.parquet

GI-TraceBench

GI-TraceBench is a source-linked benchmark for evidence-grounded scientific reasoning in gastrointestinal oncology. It connects provenance question answering, knowledge discovery, and hypothesis evaluation through shared Paper, Claim, and Evidence Span records. The benchmark contains 10,282 English questions across 50 thematic clusters.

Splits

Split	Questions	Thematic clusters
train	8,737	42
test	1,545	8

The public training split is the finalized adaptation pool used in the paper. The two splits have disjoint question IDs, normalized question texts, and thematic clusters. Only train and test are defined in the default HF configuration. The benchmark has 5,907 provenance QA, 1,910 knowledge discovery QA, and 2,465 hypothesis evaluation QA instances in total.

Load the dataset

import json
from datasets import load_dataset

dataset = load_dataset("Sereints/FindLink")
assert len(dataset["train"]) == 8737
assert len(dataset["test"]) == 1545

example = dataset["train"][0]
answer = json.loads(example["answer"])
full_record = json.loads(example["record_json"])

The same configuration works with streaming=True.

Files and representations

• data/train.parquet, data/test.parquet: the default HF loading and viewer files. They expose the question, structured answer encoded as a JSON string, task metadata, source-ID lists, and the complete record in record_json.
• train.jsonl, test.jsonl: complete structured publication records. Answers and nested evidence objects retain their native JSON structure. Read these with Python's json module; their heterogeneous nested types are not intended for automatic Arrow schema inference.
• source_splits/train.jsonl, source_splits/test.jsonl: the 7,671-question source training subset and the same 1,545-question test set. They are download artifacts for tracing the source snapshot, excluded from the default HF config. The default training pool is the 8,737-question root training file.
• evidence_store/: 1,614 paper-ID records, 5,475 Claims, 4,968 Evidence Spans, 733 associations, and 2,048 hypothesis records. These are structured support records, not a collection of complete paper PDFs. They are download artifacts, excluded from the default QA loading configuration.
• dataset_info.json, release_alignment.json, SHA256SUMS.txt: representation metadata, source checksums, and checksums for every uploaded file respectively.

The default viewer keeps qa_id, qa_group_id, qa_family, subtask_type, qa_subtype, cluster_id, language, split, and question as strings, and claim_ids, paper_ids, evidence_span_ids, association_ids, and hypothesis_ids as lists of strings. Optional fields may be null. answer and record_json are JSON strings. Decoding record_json reconstructs the corresponding structured JSONL record without dropping fields or changing scientific content.

Publication records normalize split and partition metadata to the public train/test scheme. The frozen source snapshot remains unchanged. This is a publication view of that snapshot, not a byte-for-byte copy of the source files. Natural-language uses of terms such as clinical validation are preserved.

Evaluation and limitations

The dataset supports provenance QA, knowledge discovery, and hypothesis evaluation under supplied evidence. Reference answers are provided for training and evaluation; they must not be supplied as model input during held-out testing. Use record_json or the structured JSONL for task-specific assessment fields.

Construction uses AI-assisted processing and structured reference records. The benchmark is specific to English gastrointestinal oncology literature; its generated assessment targets and evidence coverage have limitations. Candidate hypotheses are assessment objects and do not establish clinical efficacy. This dataset is intended for scientific-reasoning research.

Licensing and source attribution

The authors' contributions to GI-TraceBench are licensed under the Apache License 2.0, as selected for the Sereints/FindLink repository. This grant applies only to rights the authors are entitled to license. Third-party paper excerpts and source-derived material retain their applicable original terms and are excluded from any additional grant of rights by this dataset license. Paper IDs and Evidence Span links support source attribution. Selecting this license does not establish permission to redistribute source material; the applicable source terms must also be satisfied.

Provenance

This publication view is derived from the frozen trusted_reasoning_release_20260927 snapshot. release_alignment.json records the source-relative file paths and hashes. No model API credentials or local authentication files are part of the dataset package.