LLM Benchmarks and Tasks

If the LLM is intended to be used for a wide range of tasks, then a comprehensive benchmark such as HELM or Big-Bench is a good choice.

If the LLM is intended to be used for a specific task, such as Natural Language Inference, then a more targeted benchmark such as GLUE or SuperGLUE may be a better choice.

For fine-grained evaluation the measurement framework that are most plausible ideas in this area of LLM benchmarking are Holistic Evaluation of Language Model (HELM) and Fine-Grained Language Model Evaluation Based On Alignment Skillsets (FLASK).

Benchmarks and tasks for comparing LLMs are typically designed to assess their performance on a variety of language-related tasks, such as:

  • Language modeling: Predicting the next word in a sequence of words.

  • Text completion: Filling in the blanks in a sentence or paragraph.

  • Sentiment analysis: Identifying the sentiment of a piece of text, such as positive, negative, or neutral.

  • Question answering: Answering questions about a given passage of text.

  • Summarization: Generating a shorter version of a piece of text while preserving the key information.

  • Machine translation: Translating text from one language to another.

Some common benchmarks for comparing LLMs include:

  • GLUE (General Language Understanding Evaluation): A benchmark that evaluates LLMs on a variety of natural language processing tasks, such as question answering, sentiment analysis, and natural language inference.

  • SuperGLUE: A more challenging benchmark that extends GLUE with new tasks that require more complex reasoning and commonsense knowledge.

  • MMLU (Measuring Massive Multitask Language Understanding): A benchmark that evaluates LLMs on a wide range of tasks, including those from the humanities and hard sciences.

  • ARC (AI2 Reasoning Challenge): A benchmark that evaluates LLMs on their ability to answer grade-school science questions.

  • HellaSwag: A benchmark that evaluates LLMs on their ability to perform commonsense reasoning tasks.

In addition to these general benchmarks, there are also specific benchmarks for evaluating LLMs on particular tasks, such as Machine Translation or Natural Language Inference.

Empty Bench is a test which tests how well the model follows instructions and answers questions in a conversation.

Codex Glue is a benchmark that tests the model's coding skills by checking its ability to generate code from descriptions or fill in missing parts of program code Snippets.

When comparing LLMs, it is important to consider their performance on a variety of benchmarks and tasks. This will help to ensure that the comparison is fair and comprehensive.


Last updated