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@inproceedings{Baumartz:et:al:2024,
  author    = {Baumartz, Daniel and Konca, Maxim and Mehler, Alexander and Schrottenbacher, Patrick
               and Braunheim, Dominik},
  title     = {Measuring Group Creativity of Dialogic Interaction Systems by
               Means of Remote Entailment Analysis},
  year      = {2024},
  isbn      = {9798400705953},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3648188.3675140},
  doi       = {10.1145/3648188.3675140},
  abstract  = {We present a procedure for assessing group creativity that allows
               us to compare the contributions of human interlocutors and chatbots
               based on generative AI such as ChatGPT. We focus on everyday creativity
               in terms of dialogic communication and test four hypotheses about
               the difference between human and artificial communication. Our
               procedure is based on a test that requires interlocutors to cooperatively
               interpret a sequence of sentences for which we control for coherence
               gaps with reference to the notion of entailment. Using NLP methods,
               we automatically evaluate the spoken or written contributions
               of interlocutors (human or otherwise). The paper develops a routine
               for automatic transcription based on Whisper, for sampling texts
               based on their entailment relations, for analyzing dialogic contributions
               along their semantic embeddings, and for classifying interlocutors
               and interaction systems based on them. In this way, we highlight
               differences between human and artificial conversations under conditions
               that approximate free dialogic communication. We show that despite
               their obvious classificatory differences, it is difficult to see
               clear differences even in the domain of dialogic communication
               given the current instruments of NLP.},
  booktitle = {Proceedings of the 35th ACM Conference on Hypertext and Social Media},
  pages     = {153–-166},
  numpages  = {14},
  keywords  = {Creative AI, Creativity, Generative AI, Hermeneutics, NLP, core, core_b05, core_c08},
  location  = {Poznan, Poland},
  series    = {HT '24}
}

@inproceedings{Abrami:et:al:2024:b,
  author    = {Abrami, Giuseppe and Wontke, Dominik Alexander and Singh, Gurpreet
               and Mehler, Alexander},
  title     = {Va.Si.Li-ES: VR-based Dynamic Event Processing, Environment Change
               and User Feedback in Va.Si.Li-Lab},
  year      = {2024},
  isbn      = {9798400705953},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3648188.3675154},
  doi       = {10.1145/3648188.3675154},
  abstract  = {Flexibility, adaptability, modularity, and extensibility in the
               context of a collaborative system are critical features for multi-user
               hypertext systems. In addition to facilitating acceptance and
               increasing reusability, these features simplify development cycles
               and enable a larger range of application areas. However, especially
               in virtual 3D hypertext systems, many of the features are only
               partially available or not available at all. To fill this gap,
               we present an approach to virtual hypertext systems for the realization
               of dynamic event systems. Such an event system can be created
               and serialized simultaneously at run time regarding the modification
               of situational, environmental parameters. This includes informing
               users and allowing them to participate in the environmental dynamics
               of the system. We present Va.Si.Li-ES as a module of Va.Si.Li-Lab,
               describe several environmental scenarios that can be adapted,
               and provide use cases in the context of 3D hypertext systems.},
  booktitle = {Proceedings of the 35th ACM Conference on Hypertext and Social Media},
  pages     = {357–-368},
  numpages  = {12},
  keywords  = {Collaborative Simulation, Environmental Event System, Hypertext, Ubiq, Va.Si.Li-Lab, Virtual Reality},
  location  = {Poznan, Poland},
  series    = {HT '24}
}

@inproceedings{Boenisch:et:al:2024,
  author    = {B\"{o}nisch, Kevin and Stoeckel, Manuel and Mehler, Alexander},
  title     = {HyperCausal: Visualizing Causal Inference in 3D Hypertext},
  year      = {2024},
  isbn      = {9798400705953},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3648188.3677049},
  doi       = {10.1145/3648188.3677049},
  abstract  = {We present HyperCausal, a 3D hypertext visualization framework
               for exploring causal inference in generative Large Language Models
               (LLMs). HyperCausal maps the generative processes of LLMs into
               spatial hypertexts, where tokens are represented as nodes connected
               by probability-weighted edges. The edges are weighted by the prediction
               scores of next tokens, depending on the underlying language model.
               HyperCausal facilitates navigation through the causal space of
               the underlying LLM, allowing users to explore predicted word sequences
               and their branching. Through comparative analysis of LLM parameters
               such as token probabilities and search algorithms, HyperCausal
               provides insight into model behavior and performance. Implemented
               using the Hugging Face transformers library and Three.js, HyperCausal
               ensures cross-platform accessibility to advance research in natural
               language processing using concepts from hypertext research. We
               demonstrate several use cases of HyperCausal and highlight the
               potential for detecting hallucinations generated by LLMs using
               this framework. The connection with hypertext research arises
               from the fact that HyperCausal relies on user interaction to unfold
               graphs with hierarchically appearing branching alternatives in
               3D space. This approach refers to spatial hypertexts and early
               concepts of hierarchical hypertext structures. A third connection
               concerns hypertext fiction, since the branching alternatives mediated
               by HyperCausal manifest non-linearly organized reading threads
               along artificially generated texts that the user decides to follow
               optionally depending on the reading context.},
  booktitle = {Proceedings of the 35th ACM Conference on Hypertext and Social Media},
  pages     = {330–-336},
  numpages  = {7},
  keywords  = {3D hypertext, large language models, visualization},
  location  = {Poznan, Poland},
  series    = {HT '24},
  video     = {https://www.youtube.com/watch?v=ANHFTupnKhI}
}