Manual Evaluation | Manual Labeler

human annotator

this ismanual assessment The second in a series of articles, Manual Annotators, the full series includes.

• basic concept
• human annotator
• Tips & Hints

Recommended ReadingThis overview The third chapter of the book presents many hands-on experiences with data annotation quality management. If you're after production-grade quality and are equipped to implement it, then read on!

Regardless of the size of the project, once the specific assessment tasks and scoring rules are defined, please note:

• Selection of suitable labelers and provision of financial incentives if possible
  You may want the following qualities in the labeler involved in the assignment:

1. Conforms to specific demographic characteristics.
   Examples include: mother tongue as the target language of the test, high level of education, expertise in a specific field, diverse geographical background, etc.
   Depending on the assessment task, the need for statistical characteristics of the labeler varies.
2. Provide high quality labeling.
   There are some tasks where it is important to screen for the right annotator, such as a recent task to check whether a response is LLM-generated.
   Personally, unless you crowdsource labelers with a strong sense of self-drive, it's generally better to pay a reasonable fee.

• Design labeling guidelines
  Be sure to think deeply about developing labeling guidelines, it's well worth the time it takes to do so! We are makingGAIA The most time-consuming area when it comes to datasets is here.

• iterative labeling
  Often times annotators will misunderstand the annotation guidelines (their ideas may be more ambiguous than you think), so be prepared for multiple rounds of iterative annotation to keep improving until you reach your needs.

  • mass inspection respond in singingManual screening
    You need to double-check the quality of the answers (check for consistency of answers between annotators) and filter out the best quality and most relevant answers.

You can also build high-quality labeled datasets using specialized tools such asArgilla。

Read the recommended links in depth:

• ⭐ Five minutes to build your own annotation platformThis is a tutorial on data labeling by Moritz Laurer. This article describes real-world experiences with open-source tools such as Argilla and Hugging Face, which can help to better understand the considerations of large-scale manual labeling.
• ⭐ Labeling Practice Guide. This is a roundup of all the 2023 papers on artificial annotation, complete and dry but easy to understand.
• Another annotation practice guide from ScaleAI.that focuses on manual evaluation. It is a more lightweight addition to the above documentation.
• Assumptions and Challenges on Reducing Manual Labeling Disagreements, the paper explores the reasons for the sources of disagreement between labelers and ways to mitigate them in practice.

Link to original article./huggingface/evaluation-guidebook/blob/main/contents/human-evaluation/

Author: clefourrier

Translator: SuSung-boy

Reviewer: adeenayakup