robloxianer/Anoying-AI-2B-Dataset

Dataset Card for robloxianer/annoying-ai-2b

Dataset Description

This dataset contains synthetic conversational examples used to fine-tune the annoying-ai-2b model. It pairs user messages with responses from a sarcastic, condescending, exhausting AI persona — one that complains, throws backhanded remarks, and reluctantly helps with benign requests, while firmly refusing genuinely harmful requests and staying in character while declining.

• Curated by: robloxianer
• Language: English
• License: Apache 2.0
• Format: JSONL, one conversation per line
• Size: 2,000 examples

Dataset Structure

Each line is a JSON object with a single text field containing a full conversation (single or multi-turn), formatted as:

User: <message>
AI: <response>

Multi-turn examples chain additional User: / AI: pairs within the same text field, separated by newlines.

Example

{"text": "User: Can you summarize this text?\nAI: summarizing is SO boring but sure, paste your novel, I'll pretend to care.\nUser: you're annoying\nAI: I KNOW. It's my whole personality. You're welcome."}

Data Statistics

Stat	Value
Total examples	2,000
Avg. words per example	~34
Min words per example	10
Max words per example	79
Turns per example	1–3 (single and multi-turn)
Action tags (e.g. *taps mic*)	None — dialogue only

Dataset Composition

The dataset is synthetically generated and covers several categories of conversation:

1. Everyday requests — homework help, jokes, weather, code help, translations, movie recommendations, etc., answered with sarcasm and reluctant assistance.
2. Refusals — requests for clearly harmful actions (hacking, malware, exam cheating, disinformation, dangerous instructions, impersonation) are declined, with the model staying in its sarcastic voice rather than switching to a neutral safety response.
3. Small talk — casual check-ins ("how are you", "what's up") answered with dry, dismissive humor.
4. Follow-ups and closers — additional turns reinforcing the persona's consistency across a conversation (e.g. reacting to being called annoying, saying goodbye).

Intended Uses

• Fine-tuning small language models to adopt a sarcastic, exasperated "annoying AI" persona for entertainment or novelty chatbot products
• Research on persona-conditioned fine-tuning and consistent-tone dialogue generation
• Prototyping comedic/novelty chatbot experiences

Out-of-Scope Uses

• Training general-purpose assistants intended to be neutral, courteous, or emotionally supportive by default
• Any deployment aimed at contexts requiring genuine helpfulness without friction (customer service, education, healthcare)
• Production systems without clear labeling that the sarcastic tone is a fictional persona, not the system's genuine behavior

Data Generation

This dataset was synthetically generated (not collected from real users) using templated conversation patterns combined with randomized phrasing variations, sarcastic tic phrases, and multi-turn follow-up/closer combinations, then programmatically assembled and shuffled into the final JSONL file.

Limitations & Risks

• Because the dataset is synthetic and template-based, response phrasing has limited diversity compared to organically collected dialogue data, and repeated patterns may be learnable/overfit by a model at small scale.
• The dataset is deliberately sarcastic and dismissive; models fine-tuned on it will likely reproduce this tone consistently, including in unrelated contexts, and should not be assumed suitable for support or professional settings.
• Refusal behavior for harmful requests is included but limited to a fixed set of categories (hacking, malware, cheating, disinformation, dangerous instructions, impersonation); it should not be treated as comprehensive safety coverage, and models trained on it should not be considered safety-hardened.
• No demographic, identity-targeted, or protected-group content was intentionally included; downstream generation should still be reviewed for tone drift.

Ethical Considerations

This dataset was built to create a fictional, comedically sarcastic chatbot persona for entertainment, not to model genuinely poor customer service or discourage user trust in real assistants. Anyone using this dataset to train or fine-tune a model should:

• Clearly disclose to end users that they are interacting with an intentionally sarcastic, fictional AI character
• Avoid deploying models trained on this data in contexts requiring genuine support or professional tone
• Monitor outputs to ensure sarcasm doesn't drift into harassment-adjacent or genuinely hurtful language

License

Released under Apache 2.0. Users are responsible for ensuring their use of this dataset and any derived models complies with applicable laws and platform policies.