| --- |
| license: cc-by-4.0 |
| task_categories: |
| - question-answering |
| - text-retrieval |
| language: |
| - en |
| tags: |
| - scientific-literature |
| - citation-prediction |
| - biochemistry |
| - chemistry |
| - biotechnology |
| size_categories: |
| - n<1K |
| --- |
| |
| # BioChemCite |
|
|
| **120 citation-prediction questions in biochemistry, chemistry and |
| biotechnology.** Given a sentence from a paper with its citation blanked out, |
| name the paper being cited. |
|
|
| Built in the shape of [CiteME](https://huggingface.co/datasets/bethgelab/CiteME), |
| for the same reason and in a different literature. |
|
|
| ## Why this exists |
|
|
| CiteME measures *finding* rather than *reading*, which is the harder and less |
| measured half of a research agent. But its excerpts are machine-learning papers, |
| the densest region of any frontier model's training. A model can recognise the |
| citation and search only to confirm it, which measures recall dressed as |
| retrieval. |
|
|
| These questions sit in wet-lab and process literature, where that advantage is |
| much weaker and where the answer has to be found rather than remembered. |
|
|
| ## Where the answers come from |
|
|
| Nowhere expensive. JATS articles link every in-text citation to its reference, |
| and the reference carries a DOI: |
|
|
| ```xml |
| <xref rid="B30" ref-type="bibr">30</xref> → <ref id="B30"> … 10.1016/j.… |
| ``` |
|
|
| So the ground truth ships with the question. No annotation, no LLM judge, no |
| human labelling — and therefore nothing to disagree with. |
|
|
| ## Fields |
|
|
| | | | |
| |---|---| |
| | `id` | `dq0000` … | |
| | `excerpt` | the sentence, with the citation replaced by `[CITATION]` | |
| | `target_doi` | DOI of the cited paper — the answer | |
| | `target_title` | title of the cited paper | |
| | `source_pmcid` | the article the excerpt came from | |
| | `source_license` | always `cc by` | |
|
|
| ## How each question was filtered |
|
|
| Roughly one sentence in twenty survives: |
|
|
| - **Exactly one citation.** *"as shown previously [4,7,12]"* has three right |
| answers and no way to say which was meant. |
| - **No author named.** *"Li et al. [CITATION]"* hands over the answer. CiteME |
| obfuscates these; here they are dropped. |
| - **Makes a claim.** Methods sections cite reagent suppliers in passing. An |
| early build produced *"polymerase was obtained from Vazyme [CITATION]"* |
| pointing at a Gibson assembly paper — unfindable, and worthless as a question. |
| Methods, Materials and Protocol sections are excluded outright. |
| - **120–600 characters.** Long enough to carry a specific claim, short enough to |
| stay a citation task rather than a reading one. |
| - **Resolves to a DOI**, so scoring is exact rather than a title-matching |
| argument. |
|
|
| Capped at four questions per source article, so no single paper dominates. |
|
|
| ## Licensing |
|
|
| **Every source article is CC BY**, verified individually against Europe PMC |
| rather than assumed. This matters: about half the open-access literature in |
| these fields is CC BY-NC-ND, and lifting a sentence out of an article and |
| blanking its citation makes a derivative, which ND forbids. Filtering happens at |
| source selection, not afterwards. |
|
|
| `source_pmcid` is on every row so each excerpt traces back to the article it |
| came from. |
|
|
| ## Known limitations |
|
|
| - **Recency.** Source articles are recent Europe PMC deposits, so the cited |
| papers skew toward work published before them. Nothing here tests finding very |
| new work. |
| - **Europe PMC only**, which means life-sciences-indexed venues. Chemistry |
| published outside PMC is under-represented, and conference proceedings are |
| absent entirely. |
| - **Difficulty is not calibrated.** Some excerpts describe the cited work |
| precisely; others gesture at it. There is no per-question difficulty label and |
| no human baseline — unlike CiteME, which has one at 69.7%. |
| - **One citation per question** by construction, so nothing here tests |
| disentangling a multi-citation claim. |
|
|
| ## Building it yourself |
|
|
| The generator is at |
| [`benchmarks/build_domainqa.py`](https://github.com/jmbarrancoidener/vela) in |
| the Vela repository. Re-running it produces a different draw from current |
| Europe PMC content rather than this exact set. |
|
|
| Built for [Vela](https://github.com/jmbarrancoidener/vela), a research agent at |
| [Idener](https://idener.ai). |
|
|
