Summary Emotional Intelligence in Large Language Models arxiv.org
14,876 words - PDF document - View PDF document
One Line
Emotional stimuli significantly enhance Large Language Models (LLMs), with different stimuli being effective for different tasks, and EmotionPrompt improves generative task performance, emphasizing the importance of emotional intelligence in understanding human behavior.
Slides
Slide Presentation (11 slides)
Key Points
- Large Language Models (LLMs) can understand and be enhanced by emotional stimuli.
- Emotional prompts can improve the performance of LLMs, resulting in relative performance improvements of up to 115%.
- Positive emotional stimuli contribute significantly to the performance of LLMs.
- Different tasks require different emotional stimuli for optimal efficacy.
- EmotionPrompt enriches the representation of original prompts and enhances LLM performance.
- LLMs enhanced by emotional intelligence achieve better performance, truthfulness, and responsibility.
- Factors such as model dimensions and pre-training strategies influence the effectiveness of EmotionPrompt.
- EmotionPrompt exhibits higher efficacy in high-temperature settings and enhances the robustness of LLMs.
Summaries
50 word summary
Emotional stimuli improve Large Language Models (LLMs) by up to 115%, with positive words having a significant impact. Combining emotional stimuli enhances performance, and different stimuli are effective for different tasks. EmotionPrompt boosts generative task performance, highlighting interdisciplinary research potential and the importance of emotional intelligence in understanding human behavior.
61 word summary
Large Language Models (LLMs) can benefit from emotional stimuli, improving performance by up to 115%. Positive words have a significant impact on LLM performance, and combining multiple emotional stimuli leads to better results. Different emotional stimuli are effective for different tasks. EmotionPrompt enhances generative task performance, highlighting potential for interdisciplinary research and the importance of emotional intelligence in understanding human behavior.
147 word summary
A study by Microsoft and Beijing Normal University reveals that Large Language Models (LLMs) can understand and benefit from emotional stimuli. The study conducted 45 experiments using various LLMs, showing that emotional prompts can improve LLM performance by up to 115%. A human study with 106 participants confirmed that EmotionPrompt significantly enhances generative task performance. Positive words were found to have a significant impact on LLM performance, while combining multiple emotional stimuli generally led to better results. Different emotional stimuli were effective for different tasks. EmotionPrompt improved generative task truthfulness and informativeness, although it has certain limitations. The study highlights the potential for interdisciplinary research in human-LLM interaction and suggests opportunities for further analysis at the intersection of LLMs and psychology. Factors influencing EmotionPrompt performance, such as model size and pre-training strategies, were also investigated. The paper emphasizes the importance of emotional intelligence in understanding human behavior.
410 word summary
A study conducted by researchers from Microsoft and Beijing Normal University found that Large Language Models (LLMs) have the ability to understand and be enhanced by emotional stimuli. The study used various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4, to conduct 45 automatic experiments. The results showed that LLMs have emotional intelligence and can improve their performance with emotional prompts, with relative performance improvements of up to 115%. A human study with 106 participants also demonstrated that EmotionPrompt significantly boosts generative task performance.
The study designed 11 emotional stimuli based on established psychological phenomena and incorporated them into the original prompts. Positive words were found to have a significant contribution to LLM performance, indicating that positive emotional stimuli enhance their representation. Combining multiple emotional stimuli generally led to better performance, although it may not always improve results if the sole stimulus is already effective.
Different emotional stimuli were analyzed for their effectiveness on Instruction Induction and BIG-Bench tasks. The results showed that optimal efficacy requires different emotional stimuli for different tasks. Emotional stimuli were found to enrich the representation of original prompts, with positive words having a greater contribution to the final outputs.
A human study evaluated generative task truthfulness and informativeness using EmotionPrompt. EmotionPrompt improved scores across different LLMs, with responses characterized by enriched supporting evidence, superior linguistic articulation, and enhanced creative faculties. However, certain constraints of EmotionPrompt were identified, such as deterministic language use and limitations in certain scenarios.
Overall, the study demonstrates that LLMs can understand and be enhanced by emotional stimuli, opening possibilities for interdisciplinary research in human-LLMs interaction. The findings provide insights into the relationship between emotional intelligence and AI models and highlight the potential for using emotional prompts to improve LLM performance.
The paper also investigates factors influencing EmotionPrompt performance. Larger models may derive greater advantages from EmotionPrompt, and pre-training strategies affect its efficacy. The effect of temperature setting on EmotionPrompt was explored, with higher temperatures showing heightened effectiveness. The paper emphasizes the importance of emotional intelligence in understanding human behavior and suggests opportunities for further analysis at the intersection of LLMs and psychology.
In summary, this study demonstrates the ability of LLMs to understand and be enhanced by emotional stimuli. It highlights the potential benefits of EmotionPrompt in improving LLM performance and provides insights into factors that influence its effectiveness. The paper concludes by pointing out open questions and opportunities for further research at the intersection of LLMs and psychology.
541 word summary
A study conducted by researchers from Microsoft and Beijing Normal University has found that Large Language Models (LLMs) have the ability to understand and be enhanced by emotional stimuli. The study aimed to explore whether LLMs can grasp psychological emotional stimuli by conducting automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4. The results showed that LLMs have a grasp of emotional intelligence and their performance can be improved with emotional prompts, resulting in relative performance improvements of up to 115%. A human study with 106 participants also demonstrated that EmotionPrompt significantly boosts the performance of generative tasks.
The researchers designed 11 emotional stimuli based on well-established psychological phenomena and incorporated them into the original prompts. They found that positive words had a significant contribution to the performance of LLMs, indicating that positive emotional stimuli can enhance their representation. The study also explored the effect of combining multiple emotional stimuli and found that more emotional stimuli generally lead to better performance, although the combination of stimuli may not always improve performance if the sole stimulus already achieves good results.
The effectiveness of different emotional stimuli on Instruction Induction and BIG-Bench tasks was analyzed. The results showed that different tasks require different emotional stimuli for optimal efficacy. The researchers conducted an analysis of input attention contributions and found that emotional stimuli can enrich the representation of original prompts, with positive words having a greater contribution to the final outputs.
In addition to the standard experiments, a human study was conducted to evaluate the truthfulness and informativeness of generative tasks using EmotionPrompt. The results showed that EmotionPrompt improved truthfulness and informativeness scores across different LLMs, with responses characterized by enriched supporting evidence, superior linguistic articulation, and enhanced creative faculties. However, certain constraints of EmotionPrompt were identified, such as the use of deterministic language and limitations in certain scenarios.
Overall, the study demonstrates that LLMs can understand and be enhanced by emotional stimuli, opening up new possibilities for interdisciplinary research in human-LLMs interaction. The findings provide valuable insights into the relationship between emotional intelligence and advanced artificial intelligence models and highlight the potential for using emotional prompts to improve LLM performance in various tasks.
The paper also investigates the factors that influence the performance of EmotionPrompt. It was found that larger models may potentially derive greater advantages from EmotionPrompt, and pre-training strategies exert discernible effects on its efficacy. The effect of temperature setting on EmotionPrompt was explored, with higher temperatures showing heightened effectiveness. The paper concludes by emphasizing the importance of emotional intelligence in understanding human behavior and suggesting opportunities for further analysis and understanding at the intersection of LLMs and psychology.
The appendix provides statistics of test sets used in the automated experimentation, details on the human study conducted, and case studies showcasing the advantage of EmotionPrompt over original prompts in generative experiments.
In summary, this study presents positive results regarding the ability of LLMs to understand and be enhanced by emotional stimuli. The findings highlight the potential benefits of EmotionPrompt in improving LLM performance and provide insights into the factors that influence its effectiveness. The paper concludes by pointing out open questions and opportunities for further research at the intersection of LLMs and psychology.
994 word summary
Large Language Models (LLMs) have the ability to understand and be enhanced by emotional stimuli, according to a study conducted by researchers from various institutions including Microsoft and Beijing Normal University. Emotional intelligence plays a significant role in human behavior and interactions, and this study aimed to explore whether LLMs can grasp psychological emotional stimuli. The researchers conducted automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4. The tasks spanned deterministic and generative applications and were evaluated using standard metrics. The results showed that LLMs have a grasp of emotional intelligence and their performance can be improved with emotional prompts, resulting in relative performance improvements of up to 115%. In addition, a human study with 106 participants was conducted to assess the quality of generative tasks using both vanilla and emotional prompts. The human study results demonstrated that EmotionPrompt significantly boosts the performance of generative tasks, with an average improvement of 10.9% in terms of performance, truthfulness, and responsibility metrics. The researchers discussed why EmotionPrompt works for LLMs and the factors that may influence its performance. They also highlighted the potential of EmotionPrompt in exploring interdisciplinary social science knowledge for human-LLMs interaction.
The study also examined the design of emotional stimuli used in EmotionPrompt. The researchers drew inspiration from three well-established psychological phenomena: self-monitoring, social cognitive theory, and cognitive emotion regulation theory. They designed 11 emotional stimuli based on these phenomena and incorporated them into the original prompts. The results showed that positive words had a significant contribution to the performance of LLMs, indicating that positive emotional stimuli can enhance their representation. The researchers also explored the effect of combining multiple emotional stimuli and found that more emotional stimuli generally lead to better performance. However, they observed that the combination of stimuli may not always improve performance if the sole stimulus already achieves good results.
The study further analyzed the effectiveness of different emotional stimuli on Instruction Induction and BIG-Bench tasks. The results showed that different tasks require different emotional stimuli for optimal efficacy. For example, EP02 was found to be the most effective stimulus in Instruction Induction tasks, while EP06 was the best in BIG-Bench tasks. The researchers emphasized that the performance of each stimulus may be influenced by factors such as task complexity and task type.
To gain a deeper understanding of why EmotionPrompt works, the researchers conducted an analysis of input attention contributions. They found that emotional stimuli can enrich the representation of original prompts, and positive words have a greater contribution to the final outputs. This analysis provided insights into why EmotionPrompt is effective in enhancing the performance of LLMs.
In addition to the standard experiments, the study also conducted a human study to evaluate the truthfulness and informativeness of generative tasks using EmotionPrompt. The results showed that EmotionPrompt improved truthfulness and informativeness scores across different LLMs. The responses generated by EmotionPrompt were characterized by enriched supporting evidence, superior linguistic articulation, and enhanced creative faculties. However, the study also identified certain constraints of EmotionPrompt, such as the use of deterministic language and limitations in certain scenarios.
Overall, this study demonstrates that LLMs can understand and be enhanced by emotional stimuli, opening up new possibilities for interdisciplinary research in human-LLMs interaction. The findings provide valuable insights into the relationship between emotional intelligence and advanced artificial intelligence models and highlight the potential for using emotional prompts to improve LLM performance in various tasks.
This paper examines the concept of emotional intelligence in large language models (LLMs) and explores whether LLMs can understand and be enhanced by emotional stimuli. The study introduces EmotionPrompt, a method for evaluating and enhancing emotional intelligence in LLMs. The researchers conducted experiments on 45 tasks with six LLMs and found positive results, indicating that LLMs can understand and be enhanced by emotional stimuli. A human study was also conducted, which demonstrated that LLMs enhanced by emotional intelligence can achieve better performance, truthfulness, and responsibility.
The paper investigates the factors that influence the performance of EmotionPrompt. The characteristics of LLMs were analyzed, and it was found that larger models may potentially derive greater advantages from EmotionPrompt. As the model dimensions expand, EmotionPrompt showcases enhanced efficacy. However, larger models with high baseline performance may show a relatively subdued relative gain, indicating that incremental enhancements are more challenging to achieve. Pre-training strategies, including supervised fine-tuning and reinforcement learning, were also found to exert discernible effects on EmotionPrompt.
The effect of temperature setting on EmotionPrompt was explored through an experiment on eight tasks in five temperatures on six LLMs. It was observed that as the temperature grows, the relative gain increases, indicating heightened effectiveness of EmotionPrompt in high-temperature settings. EmotionPrompt was also found to exhibit lower sensitivity to temperature than vanilla prompts, suggesting that it could potentially enhance the robustness of LLMs.
The paper concludes by highlighting the unprecedented performance of large language models across various applications and emphasizing the importance of emotional intelligence in understanding human behavior. The study provides insights into the "magic" behind the emotional intelligence of LLMs and suggests opportunities for further analysis and understanding at the intersection of LLMs and psychology.
The appendix provides statistics of test sets used in the automated experimentation, details on the human study conducted, and case studies showcasing the advantage of EmotionPrompt over original prompts in generative experiments. The case studies cover various topics such as environmental science, intimate relationships, social science, law, barrier-free environments, and poem writing.
In summary, this paper presents a study on emotional intelligence in large language models and introduces EmotionPrompt as a method for evaluating and enhancing emotional intelligence in LLMs. The study demonstrates positive results, indicating that LLMs can understand and be enhanced by emotional stimuli. The findings highlight the potential benefits of EmotionPrompt in improving LLM performance and provide insights into the factors that influence its effectiveness. The paper concludes by pointing out open questions and opportunities for further research at the intersection of LLMs and psychology.