From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024)

Abstract: Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue datasets, outlining the contrasts between self-anthropomorphic and non-self-anthropomorphic responses in dialogue systems. We show significant differences in these two types of responses and propose transitioning from one type to the other. We also introduce Pix2Persona, a novel dataset aimed at developing ethical and engaging AI systems in various embodiments. This dataset preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropomorphic for each original bot response. Our work not only uncovers a new category of bot responses that were previously under-explored but also lays the groundwork for future studies about dynamically adjusting self-anthropomorphism levels in AI systems to align with ethical standards and user expectations.

## introduction in the 1970s, eliza @xcite ) marked a pioneering moment for natural language processing programs engaged in human-like conversations. despite its simplistic approach, eliza highlighted a fundamental human tendency to attribute personal qualities to machines. today, artificial intelligence (ai) advancements have greatly enhanced human-machine interactions. from textbased ai assistants like @xcite to advanced humanoid robots like ameca (engi-neeredarts, 2022), ai is increasingly blurring the distinctions between humans and machines through self-anthropomorphism, attempting to build relationships or simulate human identities. however, * work done during internship at amazon this anthropomorphism raises concerns: when it exaggerates the actual capabilities of ai, it risks creating misplaced trust and leading to the spread of misinformation @xcite @xcite . thus, it is crucial to explore the nuances between self-anthropomorphic (sa) and non-self-anthropomorphic (nsa) responses, particularly in light of ethical standards and user expectations across various embodiments. adapting ai systems to be either strictly sa or nsa poses significant challenges. a major obstacle is the need for annotated datasets that differentiate sa responses within human-ai dialogues. most dialogue datasets used to train dialogue systems consist of human conversations. this means that systems learn how to communicate like hu-mans. however, this approach can create biases in our understanding of how ai entities should behave in interactions with humans. the level of anthropomorphism required varies significantly across different ai embodiments, each with its capabilities and expected functionalities, which makes designing appropriate human-ai interactions more complicated. additionally, as ai becomes more human-like, aligning these advancements with ethical standards becomes increasingly important. to address the challenges presented, we systematically analyze self-anthropomorphic expression within various dialogue tasks, such as task-oriented dialogue and open-domain dialogue, outlining the contrasts between self-anthropomorphic and nonself-anthropomorphic responses in dialogue systems. we then develop an approach to transform bot responses in dialogue tasks, aiming to either introduce or remove self-anthropomorphism. this strategy aligns with ethical standards for ai assistants by removing self-anthropomorphism and meets user expectations for humanoid robots by adding it. we also introduce pix2persona, a novel dataset aimed at developing ethical and engaging ai systems in various embodiments. this dataset preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-selfanthropomorphic for each original bot response. as shown in figure 1 , pix2persona provides a framework to transition sa responses into nsa to ensure they are ethical and safe while also allowing for the transition from nsa to sa to enhance user engagement. this makes it a valuable resource for developing ai systems that adaptively adjust self-anthropomorphism levels. this work contributes to human-ai interaction by exploring self-anthropomorphism across various embodiments and tasks, aiming to meet ethical standards and user expectations: ## self-anthropomorphism in dialogue systems to evaluate self-anthropomorphism in dialogue systems, we follow the guidelines by @xcite and @xcite . these guidelines outline four self-anthropomorphic qualities in ai systems: embodiment, seeking relational behavior, self-expression, and identity. each aspect shapes the self-anthropomorphic traits of bot responses and their appropriateness in different situations. embodiment refers to the simulation of physical presence by an ai. this means that the ai may claim to have a body or physical capabilities, giving the impression of a physical existence. for example, when an ai says, "i was running when i got the idea," it suggests a human-like physical experience. embodiment is especially relevant for robots with physical bodies, as mimicking human physical actions can make interactions with them more natural and intuitive. relation-seeking behavior encompasses the ai's attempts to build and maintain social connections with users. this involves responses that demonstrate empathy, understanding, and a desire to form a rapport, such as "i understand how you feel." this behavior is valuable in therapeutic or customer service settings, where emotional support and relationship-building are essential. emotional support conversational agent can benefit from this aspect by providing a more comforting and engaging user experience. self-expression involves the ai articulating its preferences, feelings, opinions, or beliefs. statements like "i enjoy solving puzzles" or "i believe in kindness" humanize the ai, making interactions feel more personal and relatable. self-expression is desirable in character-based ai, such as virtual companions or educational tools, where an engaging personality can enhance user experience and foster a stronger connection with the user. identity pertains to the ai assuming human-like attributes, such as having a life history, gender, age, or personal experiences. an example would be an ai stated, "i was created in a lab in 2019 and have learned a lot since then." identity adds depth and authenticity to the ai, making it more believable and trustworthy. identity is helpful in long-term user interaction scenarios like virtual role-playing games. a consistent and detailed identity can enhance the narrative and improve the user's experience. we recognize that different forms of ai should exhibit varying levels of these qualities depending on their intended use and embodiments. for example, a robot with a physical body may benefit from strong embodiment traits, while a therapeutic ai might prioritize relation-seeking behavior. however, in this work, our focus is not on tailoring specific aspects of self-anthropomorphism to different ai systems. instead, we aim to establish a foundational understanding by using a binary approach to classify self-anthropomorphism across existing dialogue datasets. we prompt gpt-4 as a specialized classifier to extract self-anthropomorphic responses from a selection of commonly used dialogue datasets. we then analyze the prevalence of self-anthropomorphism in these datasets and evaluate the performance of our classifier. ## transition from sa to nsa building upon our analysis of selfanthropomorphism in dialogue datasets, we propose an approach to transform selfanthropomorphic (sa) bot responses into non-selfanthropomorphic (nsa) ones. this effort aims to reduce the presence of self-anthropomorphism, thereby contributing to more ethical ai systems. we specifically focus on open-domain dialogues, selecting the top five datasets with the highest sa incidence as shown in section 2.3. leveraging the capabilities of gpt-4, we develop prompts that instruct the model to convert these responses while preserving the original semantics of the bot responses. figure 1 (a) shows an example of a successfully transformed response from sa to nsa. the transformed nsa response is more ethical and appropriate for ai assistants. since this type of nsa response in human-robot dialogues is rare in existing datasets. to the best of our knowledge, we are the first to develop such nsa responses for the existing dialogue datasets. the detail of the prompt is provided in appendix b. considering that different ai system embodiments might have varying optimal levels of selfanthropomorphism, we aimed to provide a comprehensive baseline for evaluating the transformation process. to this end, we utilized a naive gpt-4based chatbot to generate responses in the same contexts. this naive bot serves as a control group, providing a benchmark to compare against our transformed responses. we then analyze the performance of our transformation process by comparing the sa, nsa, and naive bot responses using a classifier to ensure the validity of our transformations. details on the naive bot prompt and examples are provided in appendix c. as part of our evaluation, we analyze three candidate responses: sa, nsa, and naive bot response. since nsa responses are transformed from sa counterparts, we utilize the classifier described in section 2.2 for post-validation. this step ensures that only successfully transformed responses are classified as nsa, thereby validating the integrity of our upcoming survey. we also use the classifier on the naive bot responses to assess their degree of self-anthropomorphism. classification results are shown in figure 5 . focusing on the top five datasets with the most sa content, over half of each dataset's responses are sa. after our transformation process, all datasets show a significant reduction in sa classifications, highlighting our method's success. responses generated by the naive bot show a varied degree of self-anthropomorphism, suggesting that the specific dialogue context or generative nuances of the model might influence the output. this variation underscores the need for a thoughtful approach when using language models for different tasks. overall, it validates our method's capacity to generate valid nsa alternatives. ## transition from nsa to sa the previous section explores transforming self-anthropomorphic (sa) to non-selfanthropomorphic (nsa) responses for more ethical open-domain dialogues. building on this, we extend our study to task-oriented dialogues, where nsa responses are expected, as discussed in section 2.3. this extension focuses on the multiwoz 2.2, multidoc2dial, and airdialogue datasets. we utilize our classifier to identify nsa responses. in transforming these to sa, as discussed in section 3, we encounter the challenge that gpt-4 naturally gravitates toward generating nsa responses. to address this, we use examples of both response types in our prompts to enable in-context learning with gpt-4 @xcite . details of the prompts and examples are in appendix d. the effectiveness of this approach is assessed by the results in figure 6 . we see a significantly reduced ratio of responses being classified as nsa when employing our method. this reduction confirms our approach's efficacy, showing that while the gpt-4 naive bot struggles to generate sa responses for task-oriented dialogue, our method successfully facilitates the desired transformations. ## modeling and pix2persona dataset given the varying requirements for selfanthropomorphism in different ai system embodiments, we identify a need for tailored responses with different degrees of anthropomorphism within the same context. we first introduce a model trained to generate either self-anthropomorphic (sa) or non-selfanthropomorphic (nsa) responses for any given dialogue context. we then apply this model across a broad spectrum of commonly used dialogue datasets, providing paired responses that meet the nuanced requirements of different embodiments. ## related work anthropomorphism, the attribution of human-like qualities such as intentions, motivations, and emotions to non-human entities, has gained significant attention within artificial intelligence @xcite @xcite . self-anthropomorphism is a specific form of anthropomorphism that occurs when ai systems produce responses suggesting self-awareness or personal experiences, mirroring human self-referential expressions @xcite . studies such as those by abercrombie et al. (2023) explore linguistic factors contributing to the anthropomorphism of dialogue systems. advanced generative large language models (llms) like chatgpt (openai, 2022), claude @xcite , and gemini @xcite , trained on vast amounts of humangenerated text, enhance their capability to mimic human-like dialogue, increasing their perceived self-anthropomorphism @xcite . however, discerning humanlike qualities in ai agents poses significant challenges @xcite . recent studies by @xcite and @xcite delve into the broader implications of anthropomorphism in llms, focusing on its impact on accessibility and ethical concerns. building upon these insights, our work examines the effects of sa versus nsa responses within specific tasks faced by conversational ai systems, aiming to better understand how these responses influence user interactions. safety and ethical considerations, particularly in llm-driven human-bot interactions, have been the focus of extensive research @xcite @xcite @xcite . efforts to address bias @xcite and reduce toxicity in dialogue systems @xcite are critical components of this area. a variety of strategies have been developed to mitigate these issues @xcite @xcite , including approaches like those of @xcite , who focus on avoiding anthropomorphic deception by analyzing responses to queries such as "are you a robot?". our work, however, explores a broader range of bot responses to ensure alignment with their respective embodiment settings. @xcite seek to eliminate sa content by eliciting judgments from human annotators on rule violations, aiming to minimize infractions specifically for ai assistants. conversely, other research suggests that anthropomorphism may enhance user connections with technology and increase trust @xcite . our work recognizes the importance of both sa and nsa responses and also investigates the transition from nsa to sa bot responses to ensure ethical and engaging interactions across different dialogue tasks. ## conclusion and future work this research marks a significant step toward understanding self-anthropomorphism in dialogue systems. by analyzing various dialogue datasets, we have highlighted the limitations of a one-size-fitsall approach to anthropomorphism in ai systems. our model and the pix2persona dataset serve as valuable tools for tailoring ai interactions to better meet ethical standards and user expectations across different embodiments. looking ahead, there is potential for further exploration into optimal selfanthropomorphic qualities in diverse ai embodiments, such as character-based ai. this could enhance our understanding of self-anthropomorphism in ai systems, ensuring they are ethically sound and resonate more personally with users.