ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in
  Biographical Reasoning
Authors: name: Jasin Cekinmez, name: Omid Ghahroodi, name: Saad Fowad Chandle, name: Dhiman Gupta, name: Ehsaneddin Asgari

We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating
and improving multimodal large language models (MLLMs) in biographical
reasoning. To the best of our knowledge, this is the first work to
systematically examine LLM capabilities in biography, a critical yet
underexplored dimension of factual knowledge. At its core, AdamDB is a
multilingual and multimodal dataset covering over 4 million individuals across
geography, time, and profession, while AdamBench provides cognitively
structured evaluations based on Bloom's taxonomy, spanning six reasoning levels
in both English and native languages. To address hallucinations, particularly
for lesser-known individuals, we propose AdamRAG, a retrieval-augmented
generation system tailored to biographical contexts. Experiments show that
AdamRAG substantially improves open-source models and modestly benefits
closed-source ones, with the largest gains on lower-order reasoning. Popularity
strongly mediates accuracy, and multimodal input via face images offers
smaller, less consistent improvements than retrieval. ADAM establishes the
first benchmark and framework for cognitively, culturally, and multimodally
grounded biographical evaluation, advancing the development of multilingual,
accurate, and hallucination-resistant MLLMs.