TCMQA

Built by TechTCM.

A Traditional Chinese Medicine question-answering benchmark: 33,872 Chinese-language multiple-choice questions drawn from TCM licensing-exam material, plus a 5,250-question subset answered by 102 licensed TCM practitioners, giving a human reference point for the same items a model is scored on.

Configs

qa (default) — 33,872 rows

Field	Type	Notes
id	int64	Stable question id. Not contiguous — ids are preserved from the source collection across cleaning.
question	string	Question stem, Chinese.
choices	struct {A,B,C,D,E}	Option text. E is an empty string on 4-option questions (12,193 rows); use num_choices rather than assuming five.
num_choices	int64	4 or 5.
answer	string	Correct option letters, sorted, e.g. "B" or "BD".
num_answers	int64	1–5.
is_multi_answer	bool	True for 2,308 rows (6.8%).
explanation	string / null	Reference rationale where the source provided one (20,692 rows).
topic	string / null	Free-text topic label (23,779 rows, 5,738 distinct). Uncontrolled vocabulary — see Limitations.
difficulty_raw	int64	Source-provided integer.

doctor_annotated — 5,250 rows, 15,151 human answers

Field	Type	Notes
subset_id	int64	Unique row id within this config.
qa_id	int64 / null	Joins to qa.id. Null for 10 rows whose question was removed from qa during cleaning.
question, choices, answer, explanation, topic		Same meaning as in qa.
annotations	list of struct	One entry per doctor who answered this question.
annotations[].rater_id	int64	Pseudonymous practitioner id (102 distinct). Stable across rows, so per-rater analysis is possible.
annotations[].selected	string	Letters the rater chose, sorted.
annotations[].is_correct	bool	Exact match against answer (multi-answer questions require the full set).
annotations[].perceived_difficulty	int64	Rater's own 1–5 rating.
annotations[].categories	list of string	Rater's assignment into 10 TCM domains; empty list where the rater gave none.
num_raters	int64	3 for 4,801 rows, 2 for 299, 1 for 150.
num_correct_raters	int64	Convenience count for majority-vote scoring.

Usage

from datasets import load_dataset

qa = load_dataset("TechTCM/TCMQA", split="train")                        # 33,872
doc = load_dataset("TechTCM/TCMQA", "doctor_annotated", split="train")   # 5,250

Rendering a prompt, skipping the padded fifth option:

def render(row):
    letters = "ABCDE"[: row["num_choices"]]
    opts = "\n".join(f"{c}. {row['choices'][c]}" for c in letters)
    return f"{row['question']}\n{opts}"

Human baseline

Computed directly from the doctor_annotated config:

Measure	Value
Practitioners	102
Total answers	15,151
Per-answer accuracy (all 15,151)	62.10%
3-rater majority-vote accuracy (4,801 questions)	64.94%

Both numbers are correct and measure different things. Use 62.10% for "how well does an individual practitioner do on an item", and 64.94% when comparing a single model prediction against a panel of three practitioners on the 4,801-question subset — the latter is the appropriate baseline for most model-vs-human claims.

Doctors answered under exam-like conditions without reference material. Majority vote on a 3-rater panel uses num_correct_raters >= 2.

Credit

Built and maintained by TechTCM.

License

Released under CC BY-NC-SA 4.0. Non-commercial use, attribution required, derivatives under the same terms. See the provenance section regarding upstream material.