This is the interesting result where their GPT-2 sized transformer blows away GPT4 and Gemini 1.5 in connecting together facts The difficulty of such a task

This is the interesting result where their GPT-2 sized transformer blows away GPT4 and Gemini 1.5 in connecting together facts The difficulty of such a task is two-fold. First, the search space is large. For example, on average, each query entity connects with more than 50 facts, and each bridge entity in the ground truth proof connects with more than 900 facts. Second, there are no surface form clues to exploit and bias the search towards the ground truth proof, unlike most conventional QA benchmarks where the proof steps are transparent from the query. To test LLMs based on non-parametric memory, we translate the facts into natural language by simple templates (Appendix F). Facts/queries for each attribute are grouped/tested separately. We test both the vanilla setup where all facts (28.2K on average) are loaded into the LLM context, and the retrieval-augmented setup (5.4K facts retrieved on average) where the two-hop neighborhoods of the two query entities are retrieved, which includes enough facts to deduce the answer. We also try both standard prompting where the model answers directly, and chain-of-thought (CoT) prompting where the model is prompted to verbalize the reasoning. We test GPT-4-Turbo and Gemini-Pro-1.5, where for GPT-4-Turbo we only test the retrieval-augmented setup due to context length limit. Table 1:Results on the complex reasoning task. Direct/CoT: predict the answer directly/verbalize the reasoning steps. “+R”: retrieval augmentation. GPT-4-Turbo Gemini-Pro-1.5 Grokked Transformer Direct+R CoT+R Direct CoT Direct+R. CoT+R Accuracy (%) 33.3 31.3 28.7 11.3 37.3 12.0 99.3