Chain-of-thought prompting, by including intermediate reasoning steps in few-shot examples, elicits strong reasoning abilities in large language models on arithmetic, commonsense, and symbolic tasks.
arXiv preprint arXiv:2102.12060 , year=
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Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.
A single LLM rewrite of skill descriptions using false positive and negative cases matches manual optimization performance in production, with most other pipeline components adding little value.
citing papers explorer
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain-of-thought prompting, by including intermediate reasoning steps in few-shot examples, elicits strong reasoning abilities in large language models on arithmetic, commonsense, and symbolic tasks.
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Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes
Introduces Ex-ToxiCN-MM dataset and RIKE framework (with AKE and RIR modules) that outperforms baselines on attributing harm in ambiguous Chinese memes using C-HarmKB.
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A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization
A single LLM rewrite of skill descriptions using false positive and negative cases matches manual optimization performance in production, with most other pipeline components adding little value.