Definition of Hallucinations: Statistically plausible but factually inaccurate outputs in LLMs, resulting from probabilistic word-by-word prediction.
Current Challenges: OpenAI's findings (2025) that hallucinations are mathematically inevitable, with error rates doubling for complex queries, and their confidence-based solution leading to excessive abstention.
Literature Review:
Insights from cognitive science on creativity as controlled chaos (Dietrich, 2019), suggesting parallels with AI hallucinations.
Evolutionary biology's view of variation (e.g., genetic mutations) as a driver of innovation, applicable to AI-generated novelty.
Recent AI research on generative models' creative potential in arts and design (e.g., DALL-E studies, 2023).
Gap: Lack of a systematic framework to harness hallucinations for scientific innovation, rather than suppressing them for accuracy.
3. Theoretical Foundation: Hallucinations as Probabilistic Variation
Conceptual Basis: Redefining hallucinations as probabilistic variations, akin to genetic mutations in evolution, which provide raw material for novelty.
Human-AI Collaboration: Drawing on theories of co-creation (Amabile, 1996), where human expertise filters and refines AI-generated outputs.
Interdisciplinary Analogy: Parallels with iterative design in engineering and hypothesis generation in science, where "errors" spark breakthroughs.
