Large Language Model (LLM) Benchmarking Task

A Large Language Model (LLM) Benchmarking Task is a AI benchmarking task that assesses the performance of various Large Language Models based on defined criteria.

• Context:
  • It can (often) involve comparison in terms of accuracy, efficiency, computational power requirement, among other various factors.
  • It can be tailored to address the relative strengths and weaknesses of different models in specific applications.
  • ...
• Example(s):
  • GLUE Benchmarking Task for large language models.
  • SQuAD Benchmarking Task for question answering models.
  • MMLU (Massive Multitask Language Understanding) Benchmarking Task for ...
  • ...
• Counter-Example(s):
  • A Machine Learning Model Development Task, which focuses on the development and not the evaluation of models.
• See: Benchmarking Task, Large Language Model, Performance Evaluation.

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References

2023

• (Chen, Zaharia and Zou, 2023) ⇒ Lingjiao Chen, Matei Zaharia, and James Zou. (2023). “How is ChatGPT's Behavior Changing over Time?.” In: arXiv preprint arXiv:2307.09009. doi:10.48550/arXiv.2307.09009

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