Summary of Inadequacies Of Large Language Model Benchmarks in the Era Of Generative Artificial Intelligence, by Timothy R. Mcintosh et al.
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
by Timothy R. McIntosh, Teo Susnjak, Nalin Arachchilage, Tong Liu, Paul Watters, Malka N. Halgamuge
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers critically evaluated 23 state-of-the-art Large Language Model (LLM) benchmarks using a novel unified framework. They found significant limitations, including biases, difficulties in measuring genuine reasoning, and implementation inconsistencies. The study emphasizes the need for standardized methodologies, regulatory certainties, and ethical guidelines for LLM evaluation. The authors advocate for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs’ complex behaviors and potential risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like language. Many people want to compare different LLMs, but there’s a problem – existing tests are not fair or reliable. Researchers looked at 23 popular LLM tests and found many flaws. They think we need better ways to test LLMs so we can trust their results. This is important because LLMs will soon be used in lots of areas, like education and government. |
Keywords
» Artificial intelligence » Large language model
