fchaubard/funny_bench

FunnyBench: Stand-Up Comedy Dataset for LLM Training

LLMs aren't funny. This initiative tries to solve that. A curated dataset of 24,438 stand-up comedy transcripts with engagement metrics, designed for teaching LLMs to generate funny content. Shout out to Shofo (https://www.shofo.ai/) for providing the dataset for free for public use!

Dataset Splits

SFT (Supervised Fine-Tuning)

• 23,216 train / 1,222 test examples
• Chat format with quality-tier conditioning
• Fields: messages, tier, engagement_rate, like_count, play_count, duration_seconds, video_id, author

from datasets import load_dataset
ds = load_dataset("fchaubard/funny_bench", "sft")

DPO (Direct Preference Optimization)

• 11,607 train / 611 test preference pairs
• Pairs matched by duration bucket
• "Chosen" = higher engagement rate, "Rejected" = lower engagement rate
• Fields: prompt, chosen, rejected, chosen_engagement, rejected_engagement

from datasets import load_dataset
ds = load_dataset("fchaubard/funny_bench", "dpo")

Source Data

• 29,729 TikTok stand-up comedy clips (pre-filtered: 10,000+ likes, English, standup hashtags)
• Transcribed using NVIDIA Canary-Qwen 2.5B
• Speaker diarization via NVIDIA NeMo MSDD
• Labels: [COMEDIAN], [AUDIENCE], [LAUGHTER]

Cleaning Pipeline

Filter	Threshold	Dropped
Transcript length	80-12,000 chars	1,163
Duration	15-300 seconds	2,098
Word repetition score	<= 0.55	1,954
Unique word count	>= 15	54
ASR garbage detection	trigram loops	22
Total removed		5,291 (17.8%)
Clean dataset		24,438 (82.2%)

Quality Tiers (SFT)

Each SFT example has a quality tier based on engagement rate (likes/views):

Tier	Percentile	Engagement Rate	Count
[LEGENDARY]	Top 5%	> 21.8%	1,222
[KILLER]	75-95th	14.7-21.8%	4,888
[SOLID]	50-75th	10.8-14.7%	6,109
[WARMING_UP]	Bottom 50%	< 10.8%	12,219

At inference, prompt with [LEGENDARY] to generate top-tier comedy.

Why Engagement Rate?

Raw like counts are dominated by virality and follower counts. The engagement rate (likes/views) better captures per-viewer funniness. A clip with 1M views and 200K likes (20%) is funnier per-viewer than one with 100M views and 5M likes (5%).

SFT Format

{
  "messages": [
    {"role": "system", "content": "You are a stand-up comedian performing a live set..."},
    {"role": "user", "content": "[LEGENDARY] Perform a stand-up comedy bit."},
    {"role": "assistant", "content": "[COMEDIAN]: So where are you from?\n[AUDIENCE]: Texas!\n[COMEDIAN]: Texas? Oh man...\n[LAUGHTER]"}
  ],
  "tier": "LEGENDARY",
  "engagement_rate": 0.22,
  "like_count": 500000,
  "play_count": 2200000
}

DPO Format

{
  "prompt": [
    {"role": "system", "content": "You are a stand-up comedian..."},
    {"role": "user", "content": "Perform a stand-up comedy bit."}
  ],
  "chosen": [{"role": "assistant", "content": "...funnier transcript..."}],
  "rejected": [{"role": "assistant", "content": "...less funny transcript..."}],
  "chosen_engagement": 0.18,
  "rejected_engagement": 0.05
}

Limitations

• ASR artifacts from NVIDIA Canary-Qwen 2.5B transcription
• Comedy depends heavily on delivery and timing that text can't capture
• TikTok bias toward short-form, punchy comedy
• Engagement != funny (controversy and relatability also drive engagement)

Citation

If you use this dataset, please cite:

@misc{funnybench2026,
  title={FunnyBench: Teaching LLMs Stand-Up Comedy with Engagement-Based Preference Learning},
  year={2026}
}