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)
- Surprising events only — a model can't score by extrapolating pre-cutoff trends the way it could for a scheduled election.
- 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.) - Controls — living-person and fabricated-event rows that every run must pass.
- 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
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.