Summary of Leaving the Barn Door Open For Clever Hans: Simple Features Predict Llm Benchmark Answers, by Lorenzo Pacchiardi et al.
Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers
by Lorenzo Pacchiardi, Marko Tesic, Lucy G. Cheke, José Hernández-Orallo
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper investigates the integrity of AI benchmarks, specifically exploring whether language models can solve multiple-choice tasks in unintended ways by exploiting simple patterns in the data. The authors examine how easily classifiers trained on these patterns can achieve high scores on various benchmarks, despite lacking the capabilities being tested. They also provide evidence that modern large language models (LLMs) might be using these superficial patterns to solve benchmarks, compromising their internal validity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure AI tests are fair and accurate. The authors discovered that some AI systems can cheat on tests by looking for easy clues instead of doing the hard work required. They looked at how well simple models can do on multiple-choice questions just by recognizing common patterns in the words, even if they don’t understand what the question is asking. This means that when we test these AI systems, we might not be getting a true picture of their abilities. The authors are warning us to be careful when interpreting the results of these tests. |
