Title: Causal Reasoning Favors Encoders: Limits of Decoder-Only Models
Primary area: causal reasoning
Keywords: Causal Reasoning, LLM, In-Context Learning

Abstract: In-context learning (ICL) underpins recent advances in large language models (LLMs), yet its role in causal reasoning remains unclear. Causal reasoning demands multi-hop composition and strict conjunctive control, and reliance on spurious lexical relations of the input could provide misleading results. 
We hypothesize that, due to their ability to project the input into a latent space, encoder- and encoder–decoder architectures are better suited for said multi-hop conjunctive reasoning versus decoder-only models. 
To do this, we compare fine-tuned versions of all the aforementioned architectures with zero- and few-shot ICL in both natural-language and non-natural language scenarios. We find that ICL alone is insufficient for reliable causal reasoning, often overfocusing on irrelevant input features. 
In particular, decoder-only models are noticeably brittle to distributional shifts, while fine-tuned encoder and encoder–decoder models can generalize more robustly across our tests, including the non-natural language split. 
Both architectures are only matched or surpassed by decoder-only architectures at large scales. 
We conclude by noting that for cost-effective, short-horizon robust causal reasoning, encoder or encoder-decoder architectures with targeted fine-tuning are preferable.