Comparing two LLMs effectively requires a multi-faceted approach:
Quantitative Benchmarks:
Standard benchmarks: Use established benchmarks like GLUE, SuperGLUE, or BIGBench to assess performance on a variety of tasks like question answering, summarization, and translation.
Custom benchmarks: Design benchmarks tailored to your specific use case and priorities, focusing on relevant tasks and metrics.
Qualitative Evaluation:
Human evaluation: Recruit human evaluators to assess factors like fluency, coherence, factual accuracy, and relevance to the prompt.
Domain-specific expertise: Involve domain experts to assess performance in specialized areas like scientific writing or legal document analysis.
Consideration of factors while comparing LLM
Training data: Analyze the size, diversity, and quality of training data used by each LLM, as it can significantly impact performance.
Model architecture: Understand the underlying architecture of each LLM, as different architectures are suited for different tasks.
Task-specific metrics: Select metrics relevant to the tasks you're interested in, instead of relying on single, generic metrics.
Data Accuracy:
Ground truth comparison: Check how well labeled data aligns with its actual meaning or value. Consider using expert-annotated subsets or established benchmarks for comparison.
Error analysis: Look for patterns and types of errors, like missing values, inconsistencies, or outliers, to identify potential biases or flaws.
Data Completeness:
Missing values: Analyze the extent and distribution of missing data, as it can significantly impact model performance. Assess if imputation or exclusion is appropriate.
Data coverage: Ensure the dataset comprehensively represents the target domain and avoids skewing towards specific features or demographics.
Data Consistency:
Label consistency: Evaluate whether labels are assigned consistently across the dataset, avoiding ambiguity or subjective interpretations. Use inter-annotator agreement metrics like Cohen's kappa.
Internal consistency: Check for contradictions or logical inconsistencies within the data itself, which can confuse the model.
Data Relevance:
Task relevance: Verify if the data accurately reflects the real-world problem your model needs to address. Irrelevant data can lead to poor generalization.
Temporal relevance: Ensure data freshness aligns with the intended use case, especially for models dealing with dynamic environments.
Additional factors:
Dataset size: Consider the volume of data in relation to the model's complexity and learning capacity. Insufficient data can limit learning, while excessive data might require more resources.
Data diversity: Assess the variety of examples and scenarios the dataset covers to avoid biases and enhance model robustness.