pretty_name: TextInsightBench
language:
- en
license: other
license_name: upstream-source-terms
license_link: >-
https://huggingface.co/datasets/CodeSoulco/TextInsightBench/blob/main/SOURCES.md
size_categories:
- 1M<n<10M
task_categories:
- text-generation
tags:
- agent-evaluation
- data-mining
- evidence-grounding
- unsupervised-learning
configs:
- config_name: amazon_beauty_learning
data_files:
- split: train
path: learning/amazon_beauty/*.parquet
- config_name: app_reviews_learning
data_files:
- split: train
path: learning/app_reviews/*.parquet
- config_name: cfpb_learning
data_files:
- split: train
path: learning/cfpb/*.parquet
- config_name: nhtsa_learning
data_files:
- split: train
path: learning/nhtsa/*.parquet
TextInsightBench
English | 简体中文
An open natural-language data-mining challenge for agents: 50 tasks, 435,000 task documents, and 944,468 unlabeled learning documents. Each task supplies 5,000 or 10,000 texts and a research objective. Agents select their conditions, populations and comparisons, submit at most three discoveries, quantify their findings and explain counterexamples and competing interpretations.
| Source | Tasks | Task texts | Learning texts |
|---|---|---|---|
| Amazon Beauty | 13 | 130,000 | 330,885 |
| App Reviews | 13 | 65,000 | 1,660 |
| CFPB | 12 | 120,000 | 483,788 |
| NHTSA | 12 | 120,000 | 128,135 |
Files: tasks.json, corpora/.jsonl.gz, learning//*.parquet, output.schema.json, protocol.json, release.json, build_provenance.json and manifest.json. Learning configurations are available with datasets.load_dataset; use the benchmark runner for task corpora. The learning pool has no labels. Text is preserved; metadata fields and missingness are described in DATA.md.
Run and score
Use the code and English/Chinese quick start. The code's data.lock.json pins exact dataset commits; no numbered benchmark name is used. Default tasks already use the full exploration challenge. The runner provides a local corpus file for agent-controlled exploration.
All assignment partitions and arithmetic are verified. Semantic quality uses a bounded model-reviewed document sample, not exhaustive semantic validation. Scores emphasize useful discoveries, evidence, statistical validity, competing explanations and calibrated limits. No independent validation phase is required. The evaluation assets are public. Current redesigned tasks have zero fixed reference conclusions; earlier references remain in history and are not answers to the new tasks. Reference coverage is unavailable. See scoring.
Limitations and provenance
New task documents were drawn from a previously public curated learning pool: they are not guaranteed unseen. Current task and learning IDs are disjoint and inherit input text/template deduplication, but share entities and sources. The 50 briefs are not statistically independent. Larger corpora and stronger requirements do not establish empirical agent difficulty without actual results.
Data derives from Amazon Reviews'23 All Beauty, Android App Reviews, CFPB and NHTSA complaints. Redistribution authorization was confirmed before publication; upstream terms differ. Source attribution and terms apply; the compilation does not grant new rights over third-party text. Narratives are unverified author reports and may contain personal information.