Large Language Models ↔ CoT

Relations (1)

related 2.00 — strongly supporting 3 facts

CoT is a prompting technique specifically designed to enhance the reasoning and problem-solving capabilities of Large Language Models as described in [1] and [2], and it is a standard strategy used to evaluate these models in experimental pipelines [3].

Facts (3)

Sources

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 1 fact

claimChain-of-Thought (CoT) prompting improves problem-solving accuracy and reliability in LLMs by enabling coherent, step-by-step elaboration of thought processes.

Survey and analysis of hallucinations in large language models frontiersin.org Frontiers 1 fact

procedureThe experimental pipeline evaluates hallucinations in open-source LLMs by integrating benchmark datasets, varied prompt strategies (zero-shot, few-shot, CoT), and text generation via HuggingFace.

A framework to assess clinical safety and hallucination rates of LLMs ... nature.com Nature 1 fact

claimChain of Thought (CoT) prompting generally enhances the reasoning abilities of large language models.