Transformer language models like GPT-4 and ChatGPT have demonstrated remarkable capabilities across a wide range of tasks, sparking both admiration and concern about their potential impact. However, a recent paper titled "Faith and Fate: Limits of Transformers on Compositionality" by researchers from Allen Institute for AI, University of Washington, University of Southern California and University of Chicago takes a critical look at the limitations of these models in tasks requiring multi-step compositional reasoning. …
The realm of artificial intelligence has witnessed a significant breakthrough with the introduction of the SELF-DISCOVER framework, a novel approach that empowers Large Language Models (LLMs) to autonomously uncover and employ intrinsic reasoning structures. This advancement is poised to redefine how AI systems tackle complex reasoning challenges, offering a more efficient and interpretable method compared to traditional prompting techniques. …
The landscape of artificial intelligence (AI) in strategic games has witnessed groundbreaking achievements, with AI conquering complexities in games like chess and Go. However, a new milestone has been achieved with Cicero, an AI that exhibits human-level performance in the multifaceted board game Diplomacy, a realm that involves not just strategy, but the nuances of negotiation and human interaction. …
Orca 2 marks a significant advancement in language model development, emphasizing enhanced reasoning abilities in smaller models. This blog explores Orca 2's innovative methodologies, "Cautious Reasoning" and "Prompt Erasing," detailing their impact on AI language modeling. …
This blog post delves into the key concepts of "System 2 Attention" (S2A) mechanism, introduced in a recent paper by Jason Weston and Sainbayar Sukhbaatar from Meta, its implementation, and the various variations explored in the paper. …
The paper "Let’s Verify Step by Step" from OpenAI presents an insightful exploration into the training of large language models (LLMs) for complex multi-step reasoning tasks. Focusing on mathematical problem-solving, the authors investigate the efficacy of process supervision versus outcome supervision in training more reliable models. …