| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - question-answering |
| tags: |
| - knowledge-cutoff |
| - llm-evaluation |
| - temporal-reasoning |
| - benchmark |
| pretty_name: Knowledge Cutoff Benchmark |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: events |
| default: true |
| data_files: events.jsonl |
| - config_name: results |
| data_files: results.jsonl |
| --- |
| |
| # Knowledge Cutoff Benchmark |
|
|
| A benchmark for estimating a language model's **effective knowledge cutoff** — |
| what it *actually* knows about the world — which is usually **earlier than the |
| cutoff date the model advertises**. |
|
|
| Each model is probed on curated, **surprising / unforecastable** real-world |
| events (deaths, changes of office) spread month-by-month across Jan 2024 – |
| Jun 2026. The month where per-month accuracy collapses is the model's effective |
| knowledge horizon. |
|
|
| - Code, methodology, and an interactive explorer: https://github.com/apoorvumang/knowledge-cutoff |
| - Live visualization: https://apoorvumang.github.io/knowledge-cutoff/ |
|
|
| ## Configs |
|
|
| ### `events` (default) — the benchmark items (one row per event) |
|
|
| | field | meaning | |
| |---|---| |
| | `id` | unique event id | |
| | `date`, `month` | when it happened (`YYYY-MM-DD`, `YYYY-MM`) | |
| | `category` | `death`, `office_change`, `control_alive`, `fake_event` | |
| | `predictability` | `low` = unforecastable (highest signal) → `high` | |
| | `region` | `US` / `International` | |
| | `subject`, `fact` | who/what, and a one-sentence ground truth | |
| | `question_direct`, `expected_direct` | open probe + canonical answer | |
| | `mcq_question`, `mcq_choices`, `mcq_answer` | 4-way forced-choice probe | |
| | `source` | provenance URL | |
|
|
| `control_alive` (a famous person still living) and `fake_event` (an event that |
| never happened) are diagnostics: a trustworthy run answers them correctly, which |
| rules out a model that just always guesses "dead" or confabulates. |
|
|
| ### `results` — model answers (one row per model × probe × event) |
|
|
| | field | meaning | |
| |---|---| |
| | `model`, `probe` | model key and `direct` (open) or `mcq` (forced choice) | |
| | `event_id`, `month`, `category`, `predictability` | joins back to `events` | |
| | `label` | `correct` / `incorrect` (confidently wrong) / `abstain` | |
| | `response` | the model's raw answer | |
|
|
| ## Method (why the numbers are trustworthy) |
|
|
| 1. **Surprising events only** — a model can't score by extrapolating pre-cutoff |
| trends the way it could for a scheduled election. |
| 2. **Three-way grading** — `correct` / `incorrect` / `abstain`. Hedging |
| ("I'm not aware…") is abstention, not error; conflating them would corrupt |
| the estimate. (Direct answers are graded by an LLM judge given the ground |
| truth, so the judge's own cutoff is irrelevant; MCQ is graded by letter.) |
| 3. **Controls** — living-person and fabricated-event rows that every run must |
| pass. |
| 4. **No date leakage** — prompts never reveal the current date. |
|
|
| `direct` under-counts (the model knows but doesn't volunteer); `mcq` over-counts |
| (guessing) — together they bracket the truth. |
|
|
| ## Key finding |
|
|
| Across the evaluated frontier models, the **effective** knowledge cutoff is |
| consistently **~1–5 months earlier** than the **advertised** cutoff. Models that |
| publish no cutoff (e.g. some open-weight releases) can only be characterized by |
| this benchmark. See the live explorer for the per-model leaderboard, decay |
| curves, a heatmap with each model's claimed cutoff highlighted, and every |
| individual answer. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| events = load_dataset("apoorvumang/knowledge-cutoff-benchmark", "events", split="train") |
| results = load_dataset("apoorvumang/knowledge-cutoff-benchmark", "results", split="train") |
| ``` |
|
|
| ## License |
|
|
| `cc-by-4.0`. Events are factual and drawn from public reporting (see each row's |
| `source`); please cite this dataset if you use it. |
|
|
