| --- |
| pretty_name: LexBench-SimpleQA-Verified |
| license: |
| - apache-2.0 |
| - mit |
| tags: |
| - benchmark |
| - evaluation |
| - browser-eval |
| - web-search |
| - information-retrieval |
| - retrieval-evaluation |
| - question-answering |
| - simpleqa |
| - factuality |
| task_categories: |
| - text-retrieval |
| source_datasets: |
| - extended|google/simpleqa-verified |
| language: |
| - en |
| configs: |
| - config_name: factual-v1 |
| data_files: |
| - split: test |
| path: data/factual-v1.jsonl |
| --- |
| |
| # LexBench-SimpleQA-Verified |
|
|
| A 831-question retrieval edition of [Google SimpleQA Verified](https://huggingface.co/datasets/google/simpleqa-verified), used by [Browser Eval](https://github.com/lexmount/browser-eval) to compare search tools. It retains publisher questions, reference answers and supporting URLs so a search result list can be checked for reference-page retrieval and answer-bearing text. |
|
|
| Google DeepMind and Google Research designed the original benchmark to measure a model's factual knowledge without search tools. This derivative changes the task to retrieval evaluation. Its retrieval metrics are not official SimpleQA Verified accuracy or evidence of a model's closed-book factual knowledge. |
|
|
| | Viewer group | Source suite | Tasks | Scope | |
| | --- | --- | ---: | --- | |
| | `factual-v1` | `factual-v1` | 831 | Unchanged publisher questions of at most 128 Unicode code points, with reference answers and URLs | |
|
|
| This dataset owns the factual retrieval inputs used alongside [LexBench-Web-Search](https://huggingface.co/datasets/Lexmount/LexBench-Web-Search). Web Search references these definitions through its composition descriptor; it does not publish another copy of the questions or source receipt. |
|
|
| ## Selection and changes |
|
|
| The `factual-v1` configuration contains every question in the pinned 1,000-row publisher CSV whose `problem` has at most 128 Unicode code points, measured by Python's `len()` on the decoded string. This selects 831 questions; longer questions are excluded before collection. There is no truncation, rewriting or selection based on search results. |
|
|
| Publisher row order, question text and reference answer text are retained. Case IDs use `sqv-` followed by the publisher's `original_index`, preserving the link to OpenAI SimpleQA. Supporting URLs are split from the CSV field, deduplicated in order, and stripped of unmatched trailing closing parentheses. Topic and answer-type labels are retained. The publisher's `multi_step` and `requires_reasoning` fields are not part of these derived task records. |
|
|
| [The source manifest](definitions/factual-source.json) records the exact publisher CSV revision and SHA-256, selection rule and expected counts. [The selection implementation](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/src/lexbench_websearch/simpleqa.py) rebuilds the population from that input. This release contains no search-provider rankings, relevance grades or measured results. |
|
|
| ## Data and schema |
|
|
| | Resource | Contents | |
| | --- | --- | |
| | `data/factual-v1.jsonl` | Dataset-viewer rows: `id`, `input`, `family`, `language`, `task` | |
| | `definitions/datasets/factual-v1.json` | Complete task records and source-reference bindings | |
| | `definitions/factual-source.json` | Upstream input and deterministic selection contract | |
| | `manifest.json` | Release source commit, file sizes and SHA-256 hashes | |
|
|
| The viewer's `input` is the unchanged question. Parse `task` as JSON for `id`, `query`, `reference_answer`, `reference_urls`, `topic`, `answer_type`, provenance and derivation. Records have `family: web`, `category: factual` and `language: en`. |
|
|
| ## Load and evaluate |
|
|
| Choose a full Hugging Face commit from this dataset's revision history and retain it with your evaluation: |
|
|
| ```python |
| import json |
| from datasets import load_dataset |
| |
| rows = load_dataset( |
| "Lexmount/LexBench-SimpleQA-Verified", |
| "factual-v1", |
| split="test", |
| revision="YOUR_FULL_HF_COMMIT", |
| ) |
| task = json.loads(rows[0]["task"]) |
| query = task["query"] |
| answer, supporting_urls = task["reference_answer"], task["reference_urls"] |
| ``` |
|
|
| `definitions.tar.gz` contains the same definition bytes as `definitions/`. Browser Eval's [data lock](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/harness/benchmarks/data-lock.json) selects the installed snapshot. Follow the [Web Search guide](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/harness/web_search/README.md) to declare a profile, collect provider responses and build a report with saved evidence. The certified collector is `scripts/run_certified_benchmark.py web-search`. |
|
|
| Reference Hit@k tests whether a supplied supporting URL appears in the first k results; MRR@k measures the first such hit's reciprocal rank. Answer Match@k checks answer cues in returned text. Source references are not an exhaustive set of relevant pages, and lexical answer matches do not establish that a result correctly answers the question. Report these measures separately from graded relevance. Searchable copies of the dataset can also create contamination; Browser Eval exposes a separate contamination diagnostic while retaining the original results. |
|
|
| ## License and citations |
|
|
| The Google dataset card declares MIT. The derived definitions retain attribution to Google DeepMind / Google Research and to the original OpenAI SimpleQA, copyright 2024 OpenAI. Project-authored additions use Apache-2.0. See [LICENSE](LICENSE), [THIRD_PARTY.md](THIRD_PARTY.md) and [the retained notices](definitions/NOTICE.txt). Supporting website URLs do not grant a license to redistribute the referenced pages. |
|
|
| Cite both upstream works when using this derivative, and identify the selected dataset revision: |
|
|
| > Haas, Yona, D'Antonio, Goldshtein and Das (2025). [SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge](https://huggingface.co/datasets/google/simpleqa-verified). |
| > |
| > Wei et al. (2024). [Measuring Short-Form Factuality in Large Language Models](https://github.com/openai/simple-evals). OpenAI SimpleQA. |
|
|
| For the harness, cite [Browser Eval: Benchmarking Browsers and Web Tools for AI Agents](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/CITATION.cff). |
|
|
| [LexBench-Web-Search](https://huggingface.co/datasets/Lexmount/LexBench-Web-Search) contains the separate scenario-query suites. [LexBench-Web-Fetch](https://huggingface.co/datasets/Lexmount/LexBench-Web-Fetch) and [LexBench-WebMainBench](https://huggingface.co/datasets/Lexmount/LexBench-WebMainBench) evaluate fetching and extraction with their own task populations. |
|
|
