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Understanding the Limitations of Reasoning in LLMs
Let’s distill and learn from: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models Abstract This document explores the GSM-Symbolic benchmark, a novel framework designed to evaluate the mathematical reasoning capabilities of Large Language Models (LLMs). By addressing the limitations of traditional benchmarks, this framework provides AI engineers with structured methodologies for enhancing…
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Tab-CoT: Zero-Shot Tabular Chain Of Thought
The Tab-CoT method introduces a novel approach to reasoning in AI by utilizing a tabular format for chain-of-thought prompting. This method enhances the reasoning capabilities of large language models (LLMs) and addresses common challenges faced by AI engineers in data handling and decision-making processes.
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Enhancing System 2 Attention Mechanisms in LLMs
In the rapidly evolving field of AI engineering, traditional soft attention mechanisms in Large Language Models (LLMs) often lead to significant performance issues, such as the incorporation of irrelevant context that skews model outputs. This paper introduces System 2 Attention (S2A) as a solution to these challenges, enhancing model accuracy and reliability.
Tag: ARTIFICIAL_INTELLIGENCE
Artificial Intelligence: systems mimicking human cognition. Explore machine learning, neural networks, and AI applications across industries.