Title: Critical Confabulation: Can LLMs Hallucinate for Social Good?
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Models, AI for Social Good, Hallucination and Confabulation, Narrative Modeling, Data Contamination and Memorization, Computational Creativity, Evidence-Grounded Generation

Abstract: LLMs hallucinate, yet some confabulations can have social affordances if carefully bounded. We propose critical confabulation (inspired by critical fabulation from African American Studies), the use of LLM hallucinations to "fill-in-the-gap" for omissions in archives due to social injustice and reconstruct divergent yet evidence-bound narratives for history's "hidden figures". We simulate these gaps as an open-ended narrative cloze task: asking LLMs to generate a masked event in a character-centric timeline sourced from a prestigious archive of unpublished records. We evaluate vetted (for data contamination), fully-open models (the OLMo-2 family) and unvetted open-weight and proprietary baselines under a range of prompts designed to elicit controlled hallucination. Our findings validate LLMs' foundational narrative abilities to perform critical confabulation, and show how constrained hallucinations can support LLM applications for social good without collapsing speculation into a lack of faithfulness.

TL;DR: We introduce critical confabulation (evidence-constrained LLM hallucination for reparative storytelling) and evaluate LLMs in this task via open-ended timeline reconstruction; we show that controlled hallucinations have unique social affordances.