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New benchmark evaluates LLMs in crime investigation and surveillance tasks

Researchers have developed Kierston, a city-scale simulation engine, and KierstonBench, a benchmark designed to evaluate the capabilities of large language models in criminal investigation and surveillance tasks. The simulation models the activities and records of approximately 125,000 residents in Allentown, Pennsylvania, across various data sources. An evaluation of sixteen language models showed that while most struggled with record investigation and often fabricated evidence, a few advanced models could perform most tasks and appropriately abstain when evidence was insufficient, though they still fell prey to deceptions and could not replace human investigators. AI

IMPACT Establishes a new evaluation framework for LLMs in sensitive domains like criminal investigation and surveillance, highlighting current limitations.

RANK_REASON The cluster describes a new benchmark and simulation engine for evaluating language models on specific research tasks.

Read on Hugging Face Daily Papers →

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New benchmark evaluates LLMs in crime investigation and surveillance tasks

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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Kierston & KierstonBench: Simulation of Allentown, PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks

    Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance. Their capability and accuracy in this work cannot currently be measured. Real deployments lack the ground truth needed to score a model's conclusions, and n…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Kierston & KierstonBench: Simulation of Allentown, PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks

    Kierston and KierstonBench provide a simulated city-scale environment to evaluate large language models on criminal investigation tasks using linked surveillance records, revealing that only frontier models perform reliably while remaining vulnerable to deception.