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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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Universal Self-Consistency in LLM Generation
This paper presents Universal Self-Consistency (USC), a novel approach designed to enhance the reliability of outputs generated by large language models (LLMs). By leveraging multiple candidate responses and selecting the most consistent one, USC addresses the limitations of traditional self-consistency methods, particularly in free-form generation tasks.
Tag: MODEL_SELF_CONSISTENCY
AI technique for output reliability. Explore methods to ensure consistent responses across multiple runs, enhancing model dependability.