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This paper presents Lighthouse, a chatbot for exploring South Carolina's Civil Rights-era archives. It pairs an LLM with rule-based symbolic checks to limit harmful or untrustworthy answers, and it evaluates both single-answer factual questions and subjective questions scored against expert-aggregated "gold standard" answers. For readers, the useful takeaway is the neuro-symbolic design for grounding LLM output in a bounded source corpus, though the results will matter most once they are tested beyond this single archive.
Lighthouse: On Building a Trustworthy Digital Assistant for Exploring History with Civil Rights Archives
arXiv:2610.30158v1 Announce Type: new Abstract: Artificial intelligence (AI), as a decision-support aid, provides new tools for studying history effectively using multimodal data, including text, images, and audio. We present, to our knowledge, the first digital assistant named Lighthouse--commonly called a chatbot--for exploring the Civil Rights period of the United States using data from South Carolina. From an AI perspective, the system's novelty lies in its guiding design principles of (a) focusing on trustworthiness of content and (b) by modeling and reducing potential harm to users from generated answers. It implements these with a neuro-symbolic approach that combines neural learning through a large language model (LLM) with rule-based symbolic solving, balancing risk of answers with generality of coverage. We evaluate Lighthouse with both factual objective questions that admit a single acceptable answer as well as subjective questions. For the latter, a dataset of subjective question-answer pairs (QAs) is created by discovering a "gold standard" by aggregating answers from independent experts. Lighthouse has access to over 18k primary sources from a range of collections and can be easily extended.
Abstract:Artificial intelligence (AI), as a decision-support aid, provides new tools for studying history effectively using multimodal data, including text, images, and audio. We present, to our knowledge, the first digital assistant named Lighthouse--commonly called a chatbot--for exploring the Civil Rights period of the United States using data from South Carolina. From an AI perspective, the system's novelty lies in its guiding design principles of (a) focusing on trustworthiness of content and (b) by modeling and reducing potential harm to users from generated answers. It implements these with a neuro-symbolic approach that combines neural learning through a large language model (LLM) with rule-based symbolic solving, balancing risk of answers with generality of coverage. We evaluate Lighthouse with both factual objective questions that admit a single acceptable answer as well as subjective questions. For the latter, a dataset of subjective question-answer pairs (QAs) is created by discovering a "gold standard" by aggregating answers from independent experts. Lighthouse has access to over 18k primary sources from a range of collections and can be easily extended.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.30158 [cs.CL]
(or arXiv:2610.30158v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.30158 Focus to learn more arXiv-issued DOI via DataCite
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