--- 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