| --- |
| 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](README.zh-CN.md) |
|
|
| 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](docs/DATA.md). |
| |
| ## Run and score |
| |
| Use the [code and English/Chinese quick start](https://github.com/erwinmsmith/TextInsightBench). |
| 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](https://huggingface.co/datasets/CodeSoulco/TextInsightBench-Evaluation) |
| 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](docs/SCORING.md). |
| |
| ## 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](SOURCES.md) apply; the |
| compilation does not grant new rights over third-party text. Narratives are |
| unverified author reports and may contain personal information. |
| |
