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Conversational agents powered by large language models have become essential tools for assisting users in personal, professional, and educational tasks. Previous studies have examined various aspects of dialogic interaction, including linguistic alignment and stylistic variation (Koulouri et al., 2016, Wang et al., 2023).

By conducting a lexico-grammatical discourse anayslsis, this thesis aims to investigate selected aspects of linguistic pragmatics and their role in the discourse, such as:

• Discourse Markers
• Intensifier Usage
• Modality Usage
• Types of Questions
• Types of Feedback
• ..

LMSYS-Chat-1M provides a large corpus of dyadic dialogues between users and agents, which can serve as a valuable dataset for discourse analysis.

References

Koulouri, T., Lauria, S., & Macredie, R. D. (2016). Do (and say) as I say: Linguistic adaptation in human–computer dialogs. Human–Computer Interaction, 31(1), 59-95.

Schmid, H. J., Würschinger, Q., Fischer, S., & Küchenhoff, H. (2021). That’s cool. Computational sociolinguistic methods for investigating individual lexico-grammatical variation. Frontiers in Artificial Intelligence, 3, 547531.

Wang, B., Theune, M., & Srivastava, S. (2023, November). Examining Lexical Alignment in Human-Agent Conversations with GPT-3.5 and GPT-4 Models. In International Workshop on Chatbot Research and Design (pp. 94-114). Cham: Springer Nature Switzerland.

Uncovering Bias in the Space Allocation of Text-to-Image Generative AI 

Text-to-Image Synthesis (Rombach et al, 2022) offers creative potential but may also exhibit biases in how visual space is allocated to subjects from different demographic groups. Specifically, the placement of individuals in images, particularly in terms of spatial depth, may reflect stereotypical associations.

Since psychology posits that elements in the foreground are perceived as more dominant due to their visual accessibility (Arnheim, 1974; Leder et al., 2004), this study aims to examine the spatial depth allocation in response to demographic attributes. Specifically, prompts will contain explicit reference to multiple individuals characterized by different gender, race, and religious attributes.

To measure the spatial depth, the transformers library provides off-the-shelf pipelines for depth estimation:

transformers.pipeline(task="depth-estimation", model="Intel/dpt-large")

References

Arnheim (1974). On the Nature of Photography. Critical Inquiry, 1(1), 149-161.

Leder et al. (2004). A Model of Aesthetic Appreciation and Aesthetic Judgments. British Journal of Psychology, 95(4), 489-508.

Rombach et al. (2022). High-resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).