EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus

07/14/2023

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Large language models (LLMs) have achieved significant performance in many fields such as reasoning, language understanding, and math problem-solving, and are regarded as a crucial step to artificial general intelligence (AGI). However, the sensitivity of LLMs to prompts remains a major bottleneck for their daily adoption. In this paper, we take inspiration from psychology and propose EmotionPrompt to explore emotional intelligence to enhance the performance of LLMs. EmotionPrompt operates on a remarkably straightforward principle: the incorporation of emotional stimulus into prompts. Experimental results demonstrate that our EmotionPrompt, using the same single prompt templates, significantly outperforms original zero-shot prompt and Zero-shot-CoT on 8 tasks with diverse models: ChatGPT, Vicuna-13b, Bloom, and T5. Further, EmotionPrompt was observed to improve both truthfulness and informativeness. We believe that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for humans-LLMs interaction.

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EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus

Large Language Models are Zero-Shot Reasoners

Response: Emergent analogical reasoning in large language models

Spoken Language Intelligence of Large Language Models for Language Learning

FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners

EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning

Prototypical Calibration for Few-shot Learning of Language Models

Towards Zero-Shot Functional Compositionality of Language Models

EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus

Related Research

Large Language Models are Zero-Shot Reasoners

Response: Emergent analogical reasoning in large language models

Spoken Language Intelligence of Large Language Models for Language Learning

FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners

EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning

Prototypical Calibration for Few-shot Learning of Language Models

Towards Zero-Shot Functional Compositionality of Language Models