Motivation

The model was developed to support Wikipedia’s commitment to a neutral point of view, especially in contributions from newer editors who may be less familiar with the platform’s content policies. Initially aimed at detecting “peacock language” (overly promotional or flattering phrases), the scope of the model expanded to address a broader category of tone issues, including language that may come across as promotional, subjective, or derogatory.

This model powers Tone Check, an edit check that prompts editors to consider neutralizing their language when their edits include problematic tone. This model is also used by the Revise Tone structured task for newcomers. By surfacing tone-related suggestions at the point of editing, this model helps contributors - especially newcomers - improve the quality and neutrality of their contributions, making it easier to participate in Wikipedia in a productive and policy-aligned way.

Users and uses

Ethical considerations, caveats, and recommendations

This models relies on Multilingual BERT, a Large Language Model, that might contain certain biases.

Model

Tone Check leverages a Small Language Model (SLM) to detect the presence of promotional, derogatory, or otherwise subjective language. The SLM we are using is a BERT model, which is open source and presents its weights openly.

The model works by being fine-tuned on examples of Wikipedia revisions. It learns from instances where experienced editors have applied a specific template ("peacock") to flag tone violations, as well as instances where that template was removed. This process teaches the BERT model to identify patterns associated with appropriate and inappropriate tones based on Wikipedia's editorial standards. Under the hood, SLMs work by transforming text into high-dimensional vectors, which are then compared with the label, allowing the model to find a hyperplane that splits text into negative or positive cases.

The model was trained using 20,000 data points from 10 languages consisting of:

• Positive examples: Revisions on Wikipedia that were marked with the "peacock" template, indicating a tone policy violation.
• Negative examples: Revisions where the "peacock" template had been removed (signifying no policy violation).

The language covered are:

Small Language Models (like the one being used for Tone Check) differ from Large Language Models (LLMs) in that the former are trained to adapt for particular use cases by learning from a focused dataset. In the case of Tone Check, this means the SLM learns directly from the expertise of experienced Wikipedia volunteers. Hence, they offer more explainability and flexibility compared to LLMs. Also SLMs requires significantly fewer computational resources than its larger counterparts.

LLMs on the other hand, are designed to work for general-purposes, with limited context and through a chat or prompting interface. LLMs require a huge amount of computation resources, and their behavior is difficult to explain, due the high amount of parameters involved.

{article_title} <SEP> {language} <SEP> {article_content}

{
      "prediction": true,
      "probability": 0.831,
}

curl https://api.wikimedia.org/service/lw/inference/v1/models/edit-check:predict -X POST -d '{ "instances": [{"lang": "en", "check_type": "tone","page_title":"this is a test", "original_text": "text", "modified_text": "this is a great example of work"}]}'

{
  "predictions": [
    {
      "status_code": 200,
      "model_name": "edit-check",
      "model_version": "v1",
      "check_type": "tone",
      "language": "en",
      "page_title": "this is a test",
      "prediction": true,
      "probability": 0.831,
      "details": {}
    }
  ]
}

Data

Licenses

• Code: Apache 2.0 License
• Model: Apache 2.0 License

Citation

Cite this model as:

@misc{Tone_Check,
   title={Tone Check,
   author={Saez-Trumper, Diego and Baigutanova, Aitolkyn and Chou, Aiko and Aslam, Muniza},
   year={2024},
   url={https://meta.wikimedia.org/wiki/Machine_learning_models/Proposed/Tone_Check}
}