Summary of A Comprehensive Evaluation Of Cognitive Biases in Llms, by Simon Malberg et al.
A Comprehensive Evaluation of Cognitive Biases in LLMs
by Simon Malberg, Roman Poletukhin, Carolin M. Schuster, Georg Groh
First submitted to arxiv on: 20 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 The proposed study evaluates 30 cognitive biases in 20 state-of-the-art large language models (LLMs) under various decision-making scenarios. The researchers developed a novel general-purpose test framework for generating reliable and scalable tests for LLMs, as well as a benchmark dataset with 30,000 tests designed to detect cognitive biases in LLMs. The comprehensive assessment revealed evidence of all 30 tested biases in at least some of the evaluated LLMs, confirming and broadening previous findings on the presence of cognitive biases in LLMs. The framework code is published to encourage future research on biases in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well language models can make decisions without being influenced by certain thinking patterns. It tests 30 different biases in 20 top-performing language models and finds that all of the biases are present in some way, even if it’s just a little bit. This means that language models might not be as good at making decisions as we thought. The researchers created a new way to test these biases and made the code publicly available so other scientists can use it too. |
