Evaluation Metrics for Language Model Comparative Analysis
Defines three metrics, Accuracy, Bug Frequency, Feature Completeness, for evaluating language model generated Python game scripts.
What this file does
Defines three metrics, Accuracy, Bug Frequency, Feature Completeness, for evaluating language model generated Python game scripts.
When to use it
- Comparing outputs from different language models on coding tasks
- Establishing a standard evaluation rubric for script generation
- Reporting quantitative results in a model comparison study
- Assessing quality of generated code beyond simple pass/fail
Evaluation Metrics for Language Model Comparative Analysis
This document defines the metrics used to evaluate the performance of different language models in generating Python game scripts. The metrics focus on three key areas: Accuracy, Bug Frequency, and Feature Completeness.
1. Accuracy
Definition
Accuracy measures how closely the generated scripts align with the provided prompts or specifications.
Measurement Criteria
- Exact Match: Check if the generated script exactly matches the prompt in terms of functionality and output.
- Partial Match: Assess if key components of the prompt are addressed in the script, even if not all details are perfectly aligned.
- Scoring: Assign a score based on the percentage of prompt requirements met by the script. For instance, a script meeting all requirements scores 100%, while partial alignment scores proportionally lower.
2. Bug Frequency
Definition
Bug Frequency evaluates the number and severity of defects in the generated scripts.
Measurement Criteria
- Count of Bugs: Record the total number of bugs in the initial script output before any fixes.
- Bug Severity: Classify each bug by its impact:
- Critical: Bugs that prevent the script from running or cause major errors during execution.
- Major: Bugs that cause incorrect operations or results but do not prevent overall execution.
- Minor: Bugs that do not significantly affect the script's functionality or performance.
- Complexity of Fixes: Assess the effort required to fix the bugs (e.g., simple, moderate, complex).
3. Feature Completeness
Definition
Feature Completeness measures the extent to which the generated scripts incorporate the requested game features and mechanics.
Measurement Criteria
- Feature List: Create a checklist of all features specified in the prompt.
- Implemented Features: Mark which of the listed features are correctly implemented in the script.
- Scoring: Calculate the percentage of features successfully implemented. For example, if 8 out of 10 features are implemented, the score would be 80%.
What's inside
3 metric sections, each with definition and measurement criteria; includes scoring methods and bug severity classification.
Change this for your project
- Replace
Python game scriptswith your target output type - Replace
prompts or specificationswith your actual prompt format - Replace
game features and mechanicswith your domain-specific feature list
Where it goes
Keep alongside your test suite. Used to define and score model evaluations.
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