Delimitation

Evaluates the proper use of delimiters in prompts provided to Large Language Models.

Purpose

The Delimitation Test aims to assess whether prompts provided to the Language Learning Model (LLM) correctly use delimiters to mark different sections of the input. Well-delimited prompts help simplify the interpretation process for the LLM, ensuring that the responses are precise and accurate.

Test Mechanism

The test employs an LLM to examine prompts for appropriate use of delimiters such as triple quotation marks, XML tags, and section titles. Each prompt is assigned a score from 1 to 10 based on its delimitation integrity. Prompts with scores equal to or above the preset threshold (which is 7 by default, although it can be adjusted as necessary) pass the test.

Signs of High Risk

• Prompts missing, improperly placed, or incorrectly used delimiters, leading to misinterpretation by the LLM.
• High-risk scenarios with complex prompts involving multiple tasks or diverse data where correct delimitation is crucial.
• Scores below the threshold, indicating a high risk.

Strengths

• Ensures clarity in demarcating different components of given prompts.
• Reduces ambiguity in understanding prompts, especially for complex tasks.
• Provides a quantified insight into the appropriateness of delimiter usage, aiding continuous improvement.

Limitations

• Only checks for the presence and placement of delimiters, not whether the correct delimiter type is used for the specific data or task.
• May not fully reveal the impacts of poor delimitation on the LLM’s final performance.
• The preset score threshold may not be refined enough for complex tasks and prompts, requiring regular manual adjustment.