Summary of The Vulnerability Of Language Model Benchmarks: Do They Accurately Reflect True Llm Performance?, by Sourav Banerjee et al.
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?
by Sourav Banerjee, Ayushi Agarwal, Eishkaran Singh
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis reveals pervasive vulnerabilities across evaluation frameworks, from basic metrics to complex benchmarks like GLUE and MMLU, manifesting through benchmark exploitation, dataset contamination, and evaluation bias. We identify significant limitations in static benchmark designs, human evaluation protocols, and LLM-as-judge frameworks, compromising reliability of current performance assessments. This paper lays the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks, requiring dynamic frameworks addressing current limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can do lots of things with language, like understand what we mean when we type or talk. But right now, these models get great scores on tests because they’re good at following rules, but they don’t really understand language in a deep way. We looked at how LLMs are tested and found some big problems. The tests are too easy to cheat on, the data is messy, and people who make the tests have biases that affect the results. This means we can’t trust what the tests say about how good these models really are. So, we’re proposing new ways to test LLMs that will be harder to cheat on and will give us a better idea of how well they really understand language. |
Keywords
» Artificial intelligence » Language understanding
