| --- |
| license: other |
| task_categories: |
| - visual-question-answering |
| - image-to-text |
| tags: |
| - memes |
| - vlm |
| - benchmark |
| - humor-understanding |
| dataset_info: |
| - config_name: judgments |
| features: |
| - name: judgment_id |
| dtype: int64 |
| - name: snapshot_id |
| dtype: string |
| - name: prediction_id |
| dtype: int64 |
| - name: post_id |
| dtype: string |
| - name: model_id |
| dtype: string |
| - name: judge_model |
| dtype: string |
| - name: verdict |
| dtype: string |
| - name: reasoning |
| dtype: string |
| - name: judge_prompt_id |
| dtype: string |
| - name: judged_at |
| dtype: string |
| - name: is_latest |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 7741795 |
| num_examples: 10660 |
| download_size: 2739183 |
| dataset_size: 7741795 |
| - config_name: leaderboard |
| features: |
| - name: snapshot_id |
| dtype: string |
| - name: model_id |
| dtype: string |
| - name: judge_model |
| dtype: string |
| - name: correct |
| dtype: int64 |
| - name: incorrect |
| dtype: int64 |
| - name: total |
| dtype: int64 |
| - name: accuracy |
| dtype: float64 |
| - name: judged_by_multiple |
| dtype: int64 |
| - name: unanimous_agreements |
| dtype: int64 |
| - name: agreement_rate |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 543 |
| num_examples: 5 |
| download_size: 5148 |
| dataset_size: 543 |
| - config_name: memes |
| features: |
| - name: snapshot_id |
| dtype: string |
| - name: post_id |
| dtype: string |
| - name: title |
| dtype: string |
| - name: subreddit |
| dtype: string |
| - name: ground_truth |
| dtype: string |
| - name: image |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 170689057 |
| num_examples: 519 |
| download_size: 170630238 |
| dataset_size: 170689057 |
| - config_name: predictions |
| features: |
| - name: prediction_id |
| dtype: int64 |
| - name: snapshot_id |
| dtype: string |
| - name: post_id |
| dtype: string |
| - name: model_id |
| dtype: string |
| - name: prediction |
| dtype: string |
| - name: dataset_version |
| dtype: string |
| - name: prediction_prompt_id |
| dtype: string |
| - name: latency_ms |
| dtype: int64 |
| - name: token_count |
| dtype: int64 |
| - name: created_at |
| dtype: string |
| - name: consensus_verdict |
| dtype: string |
| - name: judge_count |
| dtype: int64 |
| - name: correct_votes |
| dtype: int64 |
| - name: incorrect_votes |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 4203146 |
| num_examples: 2785 |
| download_size: 2357404 |
| dataset_size: 4203146 |
| configs: |
| - config_name: judgments |
| data_files: |
| - split: train |
| path: judgments/train-* |
| - config_name: leaderboard |
| data_files: |
| - split: train |
| path: leaderboard/train-* |
| - config_name: memes |
| data_files: |
| - split: train |
| path: memes/train-* |
| - config_name: predictions |
| data_files: |
| - split: train |
| path: predictions/train-* |
| --- |
| |
| # BasedBench: basedBench-519-2026-07 |
|
|
| BasedBench is a VLM meme-understanding benchmark. This snapshot contains |
| 519 human-validated memes with ground-truth explanations derived from |
| Reddit comment consensus. |
|
|
| ## Task Definition |
|
|
| The task is to determine whether a model gets the joke in a meme. A correct |
| prediction identifies the relevant people, events, meme formats, media, phrases, |
| visual details, or cultural references, then reconstructs the intended setup, |
| implication, contrast, inversion, irony, wordplay, or other mechanism a viewer |
| must notice to understand the meme. |
|
|
| This benchmark does not test whether a model can produce a psychological or |
| aesthetic theory of why something is funny. |
|
|
| ## Leaderboard |
|
|
| | Model | Correct | Total | Accuracy | |
| |-------|---------|-------|----------| |
| | claude-opus-4-8 | 312 | 519 | 60.1% | |
| | google/gemini-3.1-pro-preview | 442 | 519 | 85.2% | |
| | gpt-5.5 | 419 | 519 | 80.7% | |
| | muse-spark-1.1 | 395 | 519 | 76.1% | |
| | x-ai/grok-4.3 | 254 | 519 | 48.9% | |
|
|
| ## Dataset Contents |
|
|
| The public artifact includes: |
|
|
| - Reddit post IDs, titles, and subreddit names. |
| - Meme images used as benchmark stimuli. |
| - Human-validated ground-truth explanations. |
| - Every successful model prediction retained by BasedBench for this snapshot. |
| - Every judge verdict and reasoning record for those predictions, including |
| superseded rejudgments. |
| - Derived consensus fields and leaderboard totals. |
|
|
| Raw Reddit comments, Reddit authors, review metadata, reviewer notes, consensus |
| source comment IDs, local file paths, API request metadata, internal prompts, raw |
| LLM responses, and LLM call logs are intentionally omitted. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| memes = load_dataset("montagovian/basedBench", "memes") |
| predictions = load_dataset("montagovian/basedBench", "predictions") |
| judgments = load_dataset("montagovian/basedBench", "judgments") |
| leaderboard = load_dataset("montagovian/basedBench", "leaderboard") |
| ``` |
|
|
| ## Dataset Structure |
|
|
| The dataset uses normalized long-form tables joined by `snapshot_id`, `post_id`, |
| and `prediction_id`: |
|
|
| - `memes` has one row per meme in the snapshot, with the image, title, |
| subreddit, and human-validated ground truth. |
| - `predictions` has one row per successful model prediction. It includes the |
| target model, prediction text, dataset and prompt versions where available, |
| latency, token count, timestamp, and derived consensus vote fields. |
| - `judgments` has one row per judgment attempt. It includes the target and judge |
| models, verdict, reasoning, judge prompt ID, timestamp, and an `is_latest` |
| marker. Historical rejudgments remain present. |
| - `leaderboard` has one derived row per target model, including score coverage |
| and judge agreement statistics. |
|
|
| Failed API calls and operational error messages are not benchmark predictions |
| and are omitted from the public tables. The current BasedBench database retains |
| one successful prediction per `(post_id, model_id)` pair; historical successful |
| prediction reruns that were never retained by the database cannot be exported. |
|
|
| ## Methodology |
|
|
| Ground-truth explanations are extracted from Reddit comments via LLM consensus |
| detection. A candidate ground truth must reflect at least three substantive |
| comments agreeing on the same specific explanation, and a human reviewer must |
| validate the meme before it enters a release snapshot. |
|
|
| Predictor models receive only the meme image. They do not receive the Reddit |
| title, comments, subreddit, ground truth, web search, or external tools. |
|
|
| Each model prediction is scored by an LLM judge ensemble using strict criteria: |
| the judge asks whether the model recovered the same joke as the ground truth. |
| For derived consensus fields and leaderboard accuracy, only the latest judgment |
| from each `(prediction_id, judge_model)` pair is counted. A prediction receives |
| a consensus verdict when at least two judges cast the same verdict and that |
| verdict has a strict majority; otherwise it is omitted from the leaderboard |
| denominator. All individual and historical judgments remain available in the |
| `judgments` config. |
|
|
| ## License and Rights |
|
|
| This dataset has mixed rights status, so the machine-readable Hugging Face |
| license is `other`. |
|
|
| Materials created and controlled by the BasedBench maintainers are released |
| under the MIT License. This includes the benchmark code, dataset schema, export |
| format, evaluation prompts where applicable, benchmark-specific metadata, |
| leaderboard tables, judge verdicts and reasoning, and other maintainer-authored |
| documentation and annotations to the extent the maintainers own or control those |
| materials. |
|
|
| Meme images, Reddit post titles, subreddit names, post IDs, cultural references, |
| logos, characters, screenshots, and other source artifacts may be owned by third |
| parties. The BasedBench maintainers do not claim ownership of those underlying |
| third-party materials. The MIT License for this repository does not apply to |
| those third-party materials; all such rights remain with their respective |
| owners. |
|
|
| The meme images and limited source metadata are included under a fair-use |
| rationale for research, criticism, commentary, and benchmark evaluation. The use |
| is transformative: the images are used as individual test stimuli for measuring |
| whether vision-language models understand the intended joke, not as a substitute |
| for the original posts or images. The dataset uses only the material needed to |
| support that benchmark task, omits raw comment text and authors, and does not |
| serve as a general-purpose meme archive or replacement market for the source |
| works. |
|
|
| Users are responsible for determining whether their downstream use of any |
| third-party material is permitted by law or by the relevant rights holder. If you |
| believe specific material should not be included, please contact the dataset |
| maintainers through the Hugging Face repository or the project repository. |
|
|
| ## Intended Use |
|
|
| This dataset is intended for research and evaluation of multimodal model |
| understanding, especially whether models can connect visual details, text, |
| cultural references, and joke structure. |
|
|
| It is not intended for training models to impersonate Reddit users, reconstruct |
| deleted discussions, identify commenters, or build a general meme redistribution |
| corpus. |
|
|
| ## Snapshot |
|
|
| - Snapshot name: `basedBench-519-2026-07` |
| - Snapshot ID: `ad24bf870ce6285d` |
| - Created: `2026-07-15T01:49:13.506333+00:00` |
| - Memes: 519 |
| - Successful predictions: 2785 |
| - Judgment records: 10660 |
|
|
