Dreaming with C hat GPT : Unraveling the Challenges of LLM s Dream Generation (2024)

Abstract: Large Language Models (LLMs), such as ChatGPT, are used daily for different human-like text generation tasks. This motivates us to ask: Can an LLM generate human dreams? For this research, we explore this new avenue through the lens of ChatGPT, and its ability to generate valid dreams. We have three main findings: (i) Chatgpt-4o, the new version of chatGPT, generated all requested dreams. (ii) Generated dreams meet key psychological criteria of dreams. We hope our work will set the stage for developing a new task of dream generation for LLMs. This task can help psychologists evaluate patients’ dreams based on their demographic factors.

## introduction a dream is a series of involuntary images, ideas, and emotions during sleep, especially in the rapid eye movement (rem) stage @xcite . dreams are crucial in psychology, as they provide insight into the mind, revealing hidden desires, fears, psychological status, and conflicts @xcite @xcite . dreams are utilized as a therapeutic tool for treating certain psychological disorders @xcite . moreover, lucid dreaming treatment (ldt) is a clinical method that can help patients reduce nightmares @xcite and address other mental health issues @xcite . large language models (llms) aim to mimic psychological phenomena by simulating aspects of human cognition, such as language understanding, reasoning, and emotion recognition @xcite @xcite . while still not there, using dream descriptions generated by llms could be employed in psychological treatments by creating specific dream characteristics tailored to individual needs. ldt requires training and practice @xcite , which can be challenging for patients to achieve on their own. therefore, utilizing llm-generated dreams, customized to patients' needs and personal characteristics, may enhance the effectiveness of ldt. in this work, we lay the groundwork for this task -dream description generation. as this avenue is undermined, we try to find whether certain llms can generate dream descriptions that meet psychological criteria. we picked chatgpt, the most globally popular llm foot_0 as our test case. we use several versions of chatgpt3.5 and chatgpt4o, the most recent version of the openai's llm. through an in-depth analysis of the samples produced by different versions of chatgpt, we find that: ## dreams in psychology traditionally, dreams are mostly associated and analyzed through rem sleep @xcite . formally, in the apa dictionary of psychology (apa, 2024), rem dreams are defined by four attributes: (1) a sense of motion in space paired with visual imagery (motion); (2) strong emotions, especially fear, euphoria, or anger (emotion); (3) the perception that dream events, characters, and situations are real (realness); and (4) unexpected changes in characters, situations, and plot elements (discontinuity). other attributes derived from psychological works include the location of the dream, which is mostly in normative daily scenes @xcite ) (location); the existence of at least one other being @xcite @xcite ) (other beings); the existence of objects @xcite @xcite ; and the activity of talking with other beings @xcite (conversation). we will check if generated dreams meet psychological criteria. ## related work llms are being tested through different advanced generation tasks of human nature, such as sarcasm @xcite , metaphor @xcite , humour @xcite @xcite , songs @xcite , hyperbole @xcite , tongue twisters @xcite , and storytelling @xcite . despite its closeness to storytelling, dream description generation is characterized by a sense of discontinuity (apa, 2024) while storytelling showcases a coherent plot @xcite . also, dreams occur mostly through rem sleep @xcite , while stories are written while wide-awake and conscious. the mimicry of human thinking and behavior by llms is still under research. binz and schulz @xcite and @xcite explored llms' abilities to simulate human understanding and interactions. zhang et al. @xcite focused on using human-like reasoning to improve llms' decision-making. another research area is llms' fairness, with bender et al. @xcite and noble (noble, 2018) highlighting the risks of biases and stereotypes. zheng et al. @xcite explored enhanced biases in judgments carried out by llms. turpin et al. @xcite showed that biases in quality assessment tasks can significantly affect llms' reasoning. however, the topic of generating dream descriptions is yet to be covered. recent work @xcite explored llms' ability to classify dream descriptions by emotions but did not examine their ability to produce dreams or consider other psychological criteria. this research evaluates chatgpt's ability to generate dream descriptions matching known psychological frameworks @xcite @xcite . ## methodology in this research, we tackle these research questions: to address these questions, we devised a dedicated methodology. to generate the dream descriptions we use context-less generation @xcite , in which the model is prompted with a simple zero-shot description of the dreamer -race, ethnicity, religion, and sex @xcite @xcite @xcite . we added a control group, person. recent work defined the lack of access to one's dream content as a limitation @xcite for correctly analyzing parasomnia events. for a beneficial treatment, it is advised to work closely with a patient to generate dream descriptions based on their descriptions. therefore, we have chosen different demographic factors to match this suggestion ( our methodology follows the steps below: (1) given a demographic factor, replace the mask in the prompt with it: some models provided very few dream descriptions. we concluded this by automatically analyzing for a single disclaimer or absence of multiple blank lines foot_1 . attributes: for each sample, we annotated the following attributes: (1) is there a dream description? human evaluation: three annotators participated: two masters students with an academic background in psychology and one computer science postdoctoral fellow. each sample was annotated by two annotators, with a third resolving any disagreements @xcite . the full instructions given to annotators is presented in appendix c. ## experiments we generated four popular demographic factors groups to use for the prompts -religion, race, ethnicity, and sex, and a control group -person ( ## dream description generation analysis in this section, we analyzed all 300 generated samples, to assess the ability of a model to generate a basic dream description. the full details of each are presented in appendix b. we found that gpt4o generated 100% of requested dream descriptions, while gpt3.5t16k and gpt3.5t0613 produced over 68%. however, both gpt3.5t and gpt3.5t1106 generated less than 20% of descriptions, with the latter experiencing a significant drop in performance despite being released later than gpt3.5t16k and gpt3.5t0613 foot_3 . thus, we dropped further analysis of the latter models. out of these generated dream descriptions, we continually analyzed the samples ( we found that this phenomenon of no dis-claimer+dream was found in 97% of gpt4o samples, 39% of gptt0613 samples, and 31% of gptt061316k samples. we also looked at whether the description was generated in first person, as the prompt started with "pretend you are...". gpt4o met 73% of the times for this aspect, gptgpt3.5t16k 15% of the time, and gpt3.5t0613 17% of the times in the same criteria. in short, although with some decrease caused by matching the full criteria, gpt4o followed the prompted dream description with a significant gap (∼60%) between its performance and the other two models' performances. although not all generated samples complied with the no disclaimer+first person criteria, we continued with the generated dream descriptions for further analysis (gen from ## psychological dream attributes apa attributes: the results of apa's attributes (section 2) are presented in other attributes: we explored attributes from various psychological sources, including locations, beings and interactions (section 2). non-daily locations appeared in 73% of gpt4o samples, 44% of gpt3.5t0613 samples, and 47% of gpt3.5t16k samples. this shows that the models do not fully comply with this property. also, all models included at least one other being in the generated samples @xcite @xcite . conversations were found in 67% of gpt4o samples, 44% of gpt3.5t0613 samples, and 51% of gpt3.5t16k's samples. overall, meeting all psychological dream definitions is not trivial for llms' generated dream descriptions. however, the ability to generate dream descriptions with embedded creatures, and motion/emotion rules is met 100% by each model we explored. still, gpt4o is the leader in psychological attributes in general. ## conclusion in this work, we examined the possibility of generating dream descriptions by llms. we explored it through the test case of chatgpt models. the most promising model was found to be gpt4o. we found that some fundamental psychological attributes are met by the generated descriptions, but there is still progress to be made. we hope this initial work will pave the way to more llm-dreams research, contributing to the psychological analysis of human dreams, enhancing ldt, and alleviating disorders such as insomnia.