Causal Parrots: Large Language Models May Talk Causality But Are Not Causal (2308.13067v1)

Abstract: Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that LLMs cannot be causal and give reason onto why sometimes we might feel otherwise. To this end, we define and exemplify a new subgroup of Structural Causal Model (SCM) that we call meta SCM which encode causal facts about other SCM within their variables. We conjecture that in the cases where LLM succeed in doing causal inference, underlying was a respective meta SCM that exposed correlations between causal facts in natural language on whose data the LLM was ultimately trained. If our hypothesis holds true, then this would imply that LLMs are like parrots in that they simply recite the causal knowledge embedded in the data. Our empirical analysis provides favoring evidence that current LLMs are even weak `causal parrots.'

• The paper demonstrates that LLMs echo learned correlations, failing to perform genuine causal inference.
• It introduces the concept of meta Structural Causal Models to encapsulate and analyze causal information.
• Empirical tests reveal that LLMs often falter under causal tasks defined by the Causal Hierarchy Theorem.

LLMs and Causality: An Analytical Overview

The paper "Causal Parrots: LLMs May Talk Causality But Are Not Causal" presents a critical evaluation of the causal inference capabilities of LLMs, proposing the notion that these models, while sometimes appearing causal, do not inherently possess causal understanding. Instead, the paper argues that LLMs function as "causal parrots," reiterating correlations found in their training data without true causal comprehension.

Analytical Summary

The authors rigorously examine LLMs such as GPT-3, OPT, and Luminous, exploring their potential to process causal information tethered to Pearlian causality theories. They argue against the scaling hypothesis that posits larger models with more data can inherently grasp causal relationships, emphasizing instead that causation necessitates explicit causal assumptions and model structures not present in LLMs.

Key Concepts and Contributions

1. Meta Structural Causal Models (SCM): The paper introduces the concept of a meta SCM, which encodes causal knowledge from other SCMs within its variables. The authors posit that even when LLMs correctly answer causal queries, this may result from these models encountering "correlations of causal facts" during training, rather than genuine causal inference capabilities.
2. Empirical Investigations: The authors conduct empirical evaluations under various conditions: common sense tasks, scenarios with known causal structures, and settings leveraging embeddings from knowledge bases. Despite some correct outputs, LLMs often fail or rely on ingrained correlations instead of engaging in causal reasoning.
3. Theoretical Argumentation: Insight is provided into why LLMs lack the mechanisms to infer causation based on the Causal Hierarchy Theorem, which dictates that causal inference necessitates interventions not derivable from observational data alone—a condition unmet by LLMs trained on vast text corpora.

Implications and Future Directions

The paper suggests significant implications for the use and development of LLMs in AI and machine learning. By highlighting the limitations of current LLM architectures in achieving genuine causal inference, the paper calls for the integration of explicit causal frameworks and mechanisms into AI models.

• Practical Implications: Practically, AI systems reliant on LLM outputs must be designed with caution, especially in applications requiring causal reasoning such as healthcare diagnostics or policy-making systems, which demand robust causal insights rather than surface-level statistical correlations.
• Theoretical Development: The proposal for meta SCM as a framework to encapsulate causal information suggests an avenue for future research, potentially leading to models that better simulate human reasoning processes by integrating causal layer frameworks with deep learning architectures.
• Ethical Considerations: Beyond technical limitations, the paper touches on ethical considerations, advocating transparency about what AI systems can infer and highlighting the risks of overstating their capabilities in causal reasoning.

Conclusion

The paper critically evaluates the role of LLMs in causal inference, contesting the assumption that extensive data and parameter scaling can suffice for causal understanding. By advancing the meta SCM concept and providing empirical evidence, the authors argue for a shift toward models that explicitly incorporate causal reasoning frameworks, paving the way for more sophisticated AI systems capable of meaningful causal inference.