Summary of Benchmark Inflation: Revealing Llm Performance Gaps Using Retro-holdouts, by Jacob Haimes and Cenny Wenner and Kunvar Thaman and Vassil Tashev and Clement Neo and Esben Kran and Jason Schreiber
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts
by Jacob Haimes, Cenny Wenner, Kunvar Thaman, Vassil Tashev, Clement Neo, Esben Kran, Jason Schreiber
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 abstract discusses the issue of contaminated training data for Large Language Models (LLMs) and its impact on public benchmarks. It highlights how this contamination leads to a performance gap between benchmark scores and actual capabilities. To address these issues, the authors introduce a systematic methodology for constructing holdout datasets, demonstrating their statistical indistinguishability, and comparing LLMs on both datasets. They apply this method to TruthfulQA, releasing Retro-Misconceptions as an evaluation dataset, and find that some LLMs have inflated scores by up to 16 percentage points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are like super-smart computers that can understand and generate human-like language. But did you know that the data they’re trained on is often mixed with test data? This means that when we use these models to assess their abilities, the results might not be entirely accurate. To fix this problem, researchers have developed a new way of creating a special dataset that’s just for testing. They applied this method to one popular benchmark and found that some models are overestimating their abilities by as much as 16 points! This shows how important it is to get the data right when we’re trying to understand how well these super-smart computers can really perform. |
