HyperCausal: Visualizing Causal Inference in 3D Hypertext
Pages 330 - 336
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.
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Index Terms
- HyperCausal: Visualizing Causal Inference in 3D Hypertext
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September 2024
415 pages
ISBN:9798400705953
DOI:10.1145/3648188
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Published: 10 September 2024
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