PRAXIS — Pragmatic Suggestion Mining Benchmark

PRAXIS is the largest and most domain-diverse public benchmark for actionable suggestion mining in user-generated reviews to date: ~174,860 labeled instances spanning 68 industries across consumer services, Amazon product categories, and digital/media platforms, with a fully multi-annotator-verified 15,000-row Gold+ test set as the canonical evaluation set, a three-tier pragmatic typology (form, affective modifier, specificity), and aligned suggestion spans on every positive instance for label-plus-evidence evaluation.

This is the data + code + metadata release accompanying our NeurIPS 2026 Evaluations & Datasets Track submission.

Quick start

import pandas as pd

# canonical evaluation set (15,000 rows, balanced 7,500/7,500, all 68 industries)
test = pd.read_csv("gold_test.csv")
print(test["macro_domain"].value_counts())
# Product 6858, Service 4422, Digital/media 2086, Hybrid retail 1634

# train / dev splits
train = pd.read_csv("gold_train.csv")  # 13,526 balanced rows
dev   = pd.read_csv("gold_dev.csv")    # 3,388 balanced rows

# full labeled corpus (174,860 rows; for those who want to train on more
# data than the gold splits)
full = pd.read_csv("praxis_unified_v2.csv")

# span-only positives (16,683 rows, aligned spans, for evidence localisation)
spans = pd.read_csv("span_only_v2.csv")

Files in this release

Core dataset

File	Rows	Columns	Description
gold_test.csv	15,000	24	Canonical Gold+ test set. Balanced 7,500 positive / 7,500 negative, covers all 68 industries (per-industry budget ≥ 100 except Insurance n=71 and Tax/Accounting/Investment n=66 which are coverage-saturated).
gold_train.csv	13,526	23	Balanced training split (6,763 positive / 6,763 negative), drawn from the unified pool with no overlap with gold_test.csv or gold_dev.csv.
gold_dev.csv	3,388	23	Balanced dev split (1,694 / 1,694) for hyperparameter selection.
praxis_unified_v2.csv	174,860	22	The full PRAXIS labeled pool. Includes all binary suggestion labels and, for positives, the three-tier typology + span. Use this if you want a corpus larger than gold_train.
span_only_v2.csv	16,683	23	Subset of positives that have a span localisable in the review text — supports the label-plus-evidence task.
llm_eval_subset.csv	2,040	24	Stratified subset used for the open / frontier LLM benchmarks in the paper (§"Frontier and large open-weight LLMs via a hosted cloud API"). Balanced 50/50, 30 rows per industry.
llm_eval_frontier_subset.csv	544	24	A smaller stratified subset (8 rows per industry) used for slow frontier LLMs to keep API budget within reason.

Predictions (per-row)

The bundle ships per-row predictions of every model in the leaderboard so reviewers can recompute every reported cell:

predictions/
  encoder/
    bert-base-uncased/
      test_predictions_seed{13,42,137}.csv
    roberta-base/
      test_predictions_seed{13,42,137}.csv
    deberta-base/
      test_predictions_seed{13,42,137}.csv
    roberta-large/
      test_predictions_seed{13,42,137}.csv
    roberta-prauc/
      aakash_roberta_test_predictions.csv  (recall-weighted PR-AUC re-train of Trivedi et al. 2026)
  llm/
    ollama_gpt-oss_20b.csv
    ollama_gpt-oss_120b.csv
    ollama_ministral-3_8b.csv
    ollama_ministral-3_14b.csv
    ollama_nemotron-3-nano_30b.csv
    ollama_gemma4_31b.csv
    ollama_qwen3-next_80b.csv
    ollama_kimi-k2-thinking.csv
    ollama_deepseek-v4-flash.csv
    ollama_deepseek-v4-pro.csv
    ollama_glm-4.7.csv
  leaderboard.json
  leaderboard.csv

Metadata

• croissant.json — Croissant 1.0 metadata with Responsible-AI fields.
• DATASHEET.md — Datasheet for Datasets (Gebru et al. 2021).
• LICENSE.txt — Annotation license: CC-BY-4.0 (review text subject to source-platform terms of use).

Column dictionary (gold_test.csv, gold_train.csv, gold_dev.csv)

Column	Type	Description
gold_id	string	Stable canonical row id (gold_plus_NNNNN).
gold_split	enum	gold_plus_test / gold_plus_train / gold_plus_dev.
label	int	Binary suggestion label (1 = positive, 0 = negative).
is_suggestion	bool	Same as label cast to bool.
review_id	string	Source-platform review id (Yelp business_id-style id, or row id from the Amazon source).
industry	string	One of 68 industry labels.
macro_domain	enum	Service / Product / Digital/media / Hybrid retail.
text	string	The full review text (whitespace-normalised).
suggestion_span	string	For positives, the verbatim suggestion span from the review (NONE for negatives).
tier1_form	enum	For positives: communicative form — Direct imperative, Modal/deontic, Optative, Conditional, Interrogative, Comparative.
tier2_modifiers	list	For positives: subset of {Frustrated, Hedged, Backhanded, Sarcastic} (multi-label, may be empty).
tier3_specificity	enum	For positives: Vague / Specific / Very specific.
borderline	bool	Whether the row is flagged as a borderline positive (low inter-annotator agreement).
borderline_reason	string	Free-text rationale for borderline cases.
source_file	string	Provenance: progress.csv, negatives_verified.csv, etc.
source_row	int	Row index in the source file.
resolved_review_id	string	Cleaned review id used for joining with full-text source.
review_text_source	string	Which source corpus the full text came from (labelled_Reviews1_combined_5K_per_category.csv or yelp_reviews_specific.csv).
text_match_method	string	How the row's text was looked up (e.g.\ id_industry).
text_match_candidates	int	Number of candidate matches in the source corpus.
span_in_text	bool	Whether the suggestion span is exactly substring-aligned to the review text.
span_alignment_method	string	exact_normalized / not_applicable_negative / etc.
sampling_bucket	string	The stratification bucket the row was sampled from.
eval_weight_natural_binary	float	Inverse-propensity weight for converting balanced-test-set scores back to a deployment-prevalence-weighted estimate.

praxis_unified_v2.csv shares most columns; it additionally carries prediction_prob (the production classifier's predicted probability), provenance (high-level lineage), has_text (text-availability flag), and the four boolean modifier columns (mod_frustrated, mod_hedged, mod_backhanded, mod_sarcastic).

How the data was constructed

Full details are in the paper's §3 (Dataset) and §3.2 (Annotation pipeline). The reproducible chain-of-counts is:

Source corpora

Source file	Rows	Role
labelled_Reviews1_combined_5K_per_category.csv	155,000	Upstream Amazon review corpus (~5K rows × 31 product/digital categories)
Yelp Open Dataset (service slice)	~70K	Upstream service-review corpus

Stage-1 pre-filter (recall-oriented LLM)

Output	Rows	Description
20K_positive.csv	20,650	LLM-flagged candidate positives (high-recall, low-precision pre-filter)
negatives_verified.csv	154,432	LLM-flagged-negatives sent to annotators for false-negative auditing

Stage-2 human verification

Outcome	Verified positives	Verified negatives
Among LLM-flagged positives (progress.csv, n=20,650)	14,475 confirmed positive	6,175 actually negative (pre-filter false-positive rate ≈ 30%)
Among LLM-flagged negatives (negatives_verified.csv, n=154,432)	2,303 surfaced as actually positive (pre-filter false-negative rate ≈ 1.5%)	152,129 confirmed negative

Stage-3 unified pool

After merging and deduplication:

Metric	Count
Total rows in praxis_unified_v2.csv	174,860
Verified positives	16,683
Verified negatives	158,177
Rows with full review text	173,996 (99.5%)
Industries	68
Macro-domains	4 (Product 114,991 · Digital/media 35,127 · Service 18,281 · Hybrid retail 6,461)

Stage-4 Gold+ stratification

A balanced industry-stratified Gold+ pool is carved out of the unified pool for canonical evaluation. The Gold+ split totals are:

Split	Total	Positives	Negatives
gold_test.csv	15,000	7,500	7,500
gold_train.csv	13,526	6,763	6,763
gold_dev.csv	3,388	1,694	1,694
Gold+ total	31,914	16,151	15,763

Stratification budget: ≥100 rows per industry where the pool size permitted; the two smallest-pool industries (Insurance n=71, Tax / Accounting / Investment n=66) are coverage-saturated.

Stage-5 span alignment

Every positive's verbatim suggestion span is normalised and substring-aligned to the review text; 99.9% of Gold+ positive spans align exactly. The span_only_v2.csv file (16,683 rows) is the subset of all verified positives in the unified pool with an aligned span — used for the label-plus-evidence task.

Splits

• gold_test.csv is the canonical evaluation set. Models in the leaderboard report macro-F1 on this set.
• gold_train.csv and gold_dev.csv are the recommended training data; we use them for all encoder fine-tunes in the paper.
• Models that want more training data may use praxis_unified_v2.csv, but must exclude rows whose gold_id is in gold_test.csv or gold_dev.csv, otherwise the leaderboard becomes uninterpretable.

Citing

@inproceedings{praxis2026,
  title  = {PRAXIS: A Large-Scale, Multi-Domain Benchmark for Pragmatic Suggestion Mining in User-Generated Reviews},
  author = {Anonymous},
  booktitle = {Proceedings of the NeurIPS 2026 Evaluations and Datasets Track},
  year   = {2026},
}

License

• Annotations (every column in this release except text and suggestion_span): CC-BY-4.0.
• Review text and suggestion-span snippets (text, suggestion_span): subject to the source-platform terms of use (Yelp Open Dataset License for Yelp-derived rows; Amazon ToS for Amazon-derived rows). Users are responsible for ensuring their downstream use respects those terms.

Contact

For dataset bugs, leaderboard submissions, or other inquiries, open an issue on the project's anonymised release repository (URL provided in the paper's camera-ready version).