This episode analyzes the research paper titled "Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?" by Sohee Yang, Nora Kassner, Elena Gribovskaya, Sebastian Riedel, and Mor Geva, affiliated with Google DeepMind, UCL, Google Research, and Tel Aviv University. The discussion examines whether large language models (LLMs) are capable of genuine multi-hop reasoning—connecting multiple pieces of information—without relying on shortcuts from their training data. To investigate this, the researchers developed the SOCRATES dataset, designed to evaluate the models' reasoning abilities in a shortcut-free environment.

The findings reveal that while LLMs achieve high performance in tasks involving structured data, such as recalling countries, their effectiveness drops significantly with less structured data like years. Additionally, the study highlights a notable gap between latent multi-hop reasoning and explicit Chain-of-Thought reasoning, indicating that models may internally process information differently than how they articulate their reasoning. These insights underscore the current strengths and limitations of LLMs in complex reasoning tasks and suggest directions for future advancements in artificial intelligence research.

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For more information on content and research relating to this episode please see: https://arxiv.org/pdf/2411.16679