Summary of Causality For Large Language Models, by Anpeng Wu et al.
Causality for Large Language Models
by Anpeng Wu, Kun Kuang, Minqin Zhu, Yingrong Wang, Yujia Zheng, Kairong Han, Baohong Li, Guangyi Chen, Fei Wu, Kun Zhang
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 This abstract presents a challenge in the field of artificial intelligence (AI), where large language models (LLMs) have achieved great success but are still limited by their reliance on probabilistic modeling. This approach captures patterns and stereotypes, rather than true causal relationships, making them vulnerable to biases, hallucinations, and other issues. The paper highlights the need to integrate causality into LLMs, moving beyond correlation-driven paradigms to build more reliable and ethically aligned AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have achieved great success in language tasks, but they still rely on probabilistic modeling, which captures patterns and stereotypes rather than true causal relationships. This means they can be biased towards certain demographics or social stereotypes. The paper suggests that we need to change this by integrating causality into LLMs. |
