Summary of Do Large Language Models Show Biases in Causal Learning?, by Maria Victoria Carro et al.
Do Large Language Models Show Biases in Causal Learning?
by Maria Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester, Margarita Gonzales, Mario A. Leiva, Maria Vanina Martinez, Gerardo I. Simari
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 research investigates whether large language models (LLMs) develop causal illusions in real-world and controlled contexts of causal learning and inference. The study built a dataset of over 2K samples, including correlational cases, null-contingency scenarios, and temporal information that excludes causality. LLMs were prompted to make statements or answer causal questions to evaluate their tendencies to infer causation erroneously in these structured settings. The findings show a strong presence of causal illusion bias in LLMs, with the models displaying bias at levels comparable to those observed in human subjects. However, when faced with null-contingency scenarios or temporal cues that negate causal relationships, the models exhibited significantly higher bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are trained on vast amounts of text data and can generate human-like responses. But do they also develop biases in their understanding of cause-and-effect relationships? The researchers behind this study wanted to find out. They tested LLMs on various scenarios, including ones where the correlation between two events is just a coincidence. The results show that these AI models are prone to making false assumptions about causality, similar to how humans do. This has important implications for how we use these models in applications like chatbots and language translation. |
Keywords
» Artificial intelligence » Inference » Translation
