Investigating causal understanding in LLMsDownload PDF

03 Oct 2022 (modified: 05 May 2023)CML4ImpactReaders: Everyone
Keywords: Causality, Large Language models, AI safety
TL;DR: We investigate the quality of causal world models in LLMs in multiple simple settings. We find that LLMs don't always base their answer on content but sometimes on form. We think this has implications for the safety of LLM applications.
Abstract: We investigate the quality of causal world models of LLMs in very simple settings. We test whether LLMs can identify cause and effect in natural language settings (taken from BigBench) such as “My car got dirty. I washed the car. Question: Which sentence is the cause of the other?” and in multiple other toy settings. We probe the LLM's world model by changing the presentation of the prompt while keeping the meaning constant, e.g. by changing the order of the sentences or asking the opposite question. Additionally, we test if the model can be “tricked” into giving wrong answers when we present the shot in a different pattern than the prompt. We have three findings. Firstly, larger models yield better results. Secondly, k-shot outperforms one-shot and one-shot outperforms zero-shot in standard conditions. Thirdly, LLMs perform worse in conditions where form and content differ. We conclude that the form of the presentation matters for LLM predictions or, in other words, that LLMs don't solely base their predictions on content. Finally, we detail some of the implications this research has on AI safety.
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