| ---
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| pretty_name: GI-TraceBench
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| license: apache-2.0
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| language:
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| - en
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| task_categories:
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| - question-answering
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| - text-generation
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| size_categories:
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| - 10K<n<100K
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| tags:
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| - science
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| - biomedical
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| - provenance
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| - evidence-grounded-reasoning
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| configs:
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| - config_name: default
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| default: true
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| data_files:
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| - split: train
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| path: data/train.parquet
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| - split: test
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| path: data/test.parquet
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| ---
|
|
|
| # GI-TraceBench
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|
|
| GI-TraceBench is a source-linked benchmark for evidence-grounded scientific reasoning
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| in gastrointestinal oncology. It connects provenance question answering, knowledge
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| discovery, and hypothesis evaluation through shared Paper, Claim, and Evidence Span
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| records. The benchmark contains 10,282 English questions across 50 thematic clusters.
|
|
|
| ## Splits
|
|
|
| | Split | Questions | Thematic clusters |
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| | --- | ---: | ---: |
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| | train | 8,737 | 42 |
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| | test | 1,545 | 8 |
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|
|
| The public training split is the finalized adaptation pool used in the paper.
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| The two splits have disjoint question IDs, normalized question texts, and thematic
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| clusters. Only `train` and `test` are defined in the default HF configuration.
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| The benchmark has 5,907 provenance QA, 1,910 knowledge discovery QA, and 2,465
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| hypothesis evaluation QA instances in total.
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|
|
| ## Load the dataset
|
|
|
| ```python
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| import json
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| from datasets import load_dataset
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|
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| dataset = load_dataset("Sereints/FindLink")
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| assert len(dataset["train"]) == 8737
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| assert len(dataset["test"]) == 1545
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|
|
| example = dataset["train"][0]
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| answer = json.loads(example["answer"])
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| full_record = json.loads(example["record_json"])
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| ```
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|
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| The same configuration works with `streaming=True`.
|
|
|
| ## Files and representations
|
|
|
| - `data/train.parquet`, `data/test.parquet`: the default HF loading and viewer
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| files. They expose the question, structured answer encoded as a JSON string,
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| task metadata, source-ID lists, and the complete record in `record_json`.
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| - `train.jsonl`, `test.jsonl`: complete structured publication records. Answers
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| and nested evidence objects retain their native JSON structure. Read these
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| with Python's `json` module; their heterogeneous nested types are not intended
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| for automatic Arrow schema inference.
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| - `source_splits/train.jsonl`, `source_splits/test.jsonl`: the 7,671-question
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| source training subset and the same 1,545-question test set. They are download
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| artifacts for tracing the source snapshot, excluded from the default HF config.
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| The default training pool is the 8,737-question root training file.
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| - `evidence_store/`: 1,614 paper-ID records, 5,475 Claims, 4,968 Evidence Spans,
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| 733 associations, and 2,048 hypothesis records. These are structured support
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| records, not a collection of complete paper PDFs. They are download artifacts,
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| excluded from the default QA loading configuration.
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| - `dataset_info.json`, `release_alignment.json`, `SHA256SUMS.txt`: representation
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| metadata, source checksums, and checksums for every uploaded file respectively.
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|
|
| The default viewer keeps `qa_id`, `qa_group_id`, `qa_family`, `subtask_type`,
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| `qa_subtype`, `cluster_id`, `language`, `split`, and `question` as strings, and
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| `claim_ids`, `paper_ids`, `evidence_span_ids`, `association_ids`, and `hypothesis_ids`
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| as lists of strings. Optional fields may be null. `answer` and `record_json` are
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| JSON strings. Decoding `record_json` reconstructs the corresponding structured
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| JSONL record without dropping fields or changing scientific content.
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|
|
| Publication records normalize split and partition metadata to the public
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| train/test scheme. The frozen source snapshot remains unchanged. This is a
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| publication view of that snapshot, not a byte-for-byte copy of the source files.
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| Natural-language uses of terms such as clinical validation are preserved.
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|
|
| ## Evaluation and limitations
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|
|
| The dataset supports provenance QA, knowledge discovery, and hypothesis
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| evaluation under supplied evidence. Reference answers are provided for training
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| and evaluation; they must not be supplied as model input during held-out testing.
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| Use `record_json` or the structured JSONL for task-specific assessment fields.
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|
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| Construction uses AI-assisted processing and structured reference records.
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| The benchmark is specific to English gastrointestinal oncology literature;
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| its generated assessment targets and evidence coverage have limitations.
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| Candidate hypotheses are assessment objects and do not establish clinical
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| efficacy. This dataset is intended for scientific-reasoning research.
|
|
|
| ## Licensing and source attribution
|
|
|
| The authors' contributions to GI-TraceBench are licensed under the
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| [Apache License 2.0](LICENSE), as selected for the `Sereints/FindLink` repository.
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| This grant applies only to rights the authors are entitled to license.
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| Third-party paper excerpts and source-derived material retain their applicable
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| original terms and are excluded from any additional grant of rights by this
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| dataset license. Paper IDs and Evidence Span links support source attribution.
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| Selecting this license does not establish permission to redistribute source
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| material; the applicable source terms must also be satisfied.
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|
|
| ## Provenance
|
|
|
| This publication view is derived from the frozen
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| `trusted_reasoning_release_20260927` snapshot. `release_alignment.json` records
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| the source-relative file paths and hashes. No model API credentials or local
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| authentication files are part of the dataset package.
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|
|
