Podcast Summary
Enhancing reasoning abilities of language models: Researchers at Microsoft explored methods to make language models more cautious reasoners by teaching them CATU. The approach led to improvements in reasoning abilities as shown in evaluation benchmarks and model outputs.
A group of researchers, including Sadface 1, Orindam Mitra, Luciano del Coro, Shweti Mahajan, Andres Codas, Clarisse Simos, Sahaj Agarwal, Shushi Chen, Anastasia Rasdavidina, Eric Jones, Kriti Agarwal, Hamid Palanji, Guo Ching Jeong, and Corby Rosset, while working at Microsoft, explored methods to enhance the reasoning abilities of smaller language models. They published their findings in a paper available under a CC 4.0 license. The paper discusses the importance of teaching or CATU (Cautious and Thorough Understanding) to make language models more careful reasoners. The authors evaluated their approach using various metrics, including Agival subtask metrics, Big Bench hard subtask metrics, and grounding in abstractive summarization. They also assessed safety and used specific prompts in their evaluation. The results showed improvements in the model's ability to reason, as demonstrated by examples from evaluation benchmarks and corresponding model outputs. However, the authors acknowledged limitations and potential future research directions. Overall, the study emphasizes the importance of developing more capable and cautious language models to enhance their reasoning abilities. For more details, you can refer to the paper's abstract, introduction, preliminaries, technical details, experimental setup, evaluation results, limitations, conclusions, and references. Visit hackernoon.com to read, write, learn, and publish.