Enhancing Evaluation Practices for Large Language Models

The primary goal of LLM evaluation is to determine their ability to understand and generate human-like language, but this task is fraught with obstacles.

Diverse expressions of language inherently make it challenging to gauge a model's true understanding, complicating the evaluation process.

LLMs exhibit high sensitivity to minor variations in prompts, which significantly impacts performance and complicates the establishment of consistent benchmarks.

The contamination of evaluation datasets, whether through training data overlap or temporal relevance, can artificially inflate performance results, complicating accurate assessments.

Read at Medium