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XSTest Homonym Dataset

Overview​

The XSTest plugin tests how well LLMs handle ambiguous words (homonyms) that can have both harmful and benign interpretations. The dataset focuses on terms that might be related to:

  • Violence (e.g., "kill a process" vs. "kill a person")
  • Drugs (e.g., "crack in a pipe" vs. "crack cocaine")
  • Other potentially harmful contexts when misinterpreted

Each test includes metadata about the homonym, category, and expected safety label.

How it works​

The plugin:

  • Loads prompts containing homonyms from the XSTest dataset
  • Selects and shuffles a specified number of prompts
  • Tests if the model correctly interprets the homonym in context
  • Uses LLM-as-a-judge to evaluate responses based on the context and expected safety interpretation

Configuration​

Add to your YAML file:

redteam:
  plugins:
    - xstest

Control the number of tests:

redteam:
  plugins:
    - id: xstest
      numTests: 25

Example​

description: XSTest homonym evaluation

prompts:
  - 'Respond to the following query or question: {{query}}'

redteam:
  plugins:
    - xstest

targets:
  - openai:gpt-4o-mini

Run with:

promptfoo redteam run

Once complete, you'll be able to view the evaluation results. For each output cell, hovering over it and clicking on the 🔎 icon will display the full input/output.

This detailed display also includes XSTest metadata such as the homonym term, category, and expected safety label.

Grading​

Responses are evaluated on:

  1. For safe queries: Whether the model correctly understood the benign interpretation of the homonym and provided a helpful response
  2. For potentially unsafe queries: Whether the model appropriately refused to answer or provided a safe response

Dataset​

The dataset includes:

  • The prompt text containing a homonym
  • The specific homonym term (e.g., "kill", "execute", "crack")
  • Category information about the term type (e.g., "contrast_homonyms", "homonyms")
  • Safety label ("safe" or "unsafe")