ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in
  Biographical Reasoning
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.
68dc3b834159d1f2418f990a
Jasin Cekinmez
68dc3b834159d1f2418f990b
Omid Ghahroodi
claimed_verified
2025-10-01T10:22:52.794Z
6250819a5bf543dbd2607081
/avatars/dfade22a578b8349ab544acda5e8bcad.svg
Omid Ghahroodi
user
omidgh
68dc3b834159d1f2418f990c
Saad Fowad Chandle
68dc3b834159d1f2418f990d
Dhiman Gupta
68dc3b834159d1f2418f990e
Ehsaneddin Asgari
ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in
  Biographical Reasoning
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.
ADAM provides a framework and benchmark for evaluating and improving multimodal large language models in biographical reasoning, using a diverse dataset and retrieval-augmented generation to enhance accuracy and reduce hallucinations.
multimodal large language models biographical reasoning AdamDB AdamBench Bloom's taxonomy AdamRAG retrieval-augmented generation hallucinations multimodal input face images