@inproceedings{yao-etal-2026-arrest,
title = "Before the Arrest: Benchmarking {LLM}s on Criminal Profiling from Incomplete Evidence",
author = "Yao, Yutong and
Yanjie, Cao and
Chen, Guanhua and
Xu, Yang and
Wu, Junchao and
Wu, Zeyu and
Chao, Lidia S. and
Wong, Derek F.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2263/",
pages = "48462--48483",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest scenarios where the suspect{'}s identity is already known, leaving the critical pre-arrest challenge of inferring suspect characteristics from incomplete evidence largely unexplored. To fill this gap, we introduce the \textbf{P}rofiling, \textbf{I}nvestigation, and \textbf{J}udgment (\textbf{PIJ}) benchmark, comprising 2,500 real homicide cases from five countries. PIJ evaluates LLMs across three tasks that span the entire criminal investigation pipeline: criminal profiling, which requires abductive reasoning to infer suspect attributes from fragmentary scene evidence, crime process reconstruction, which tests structured information extraction, and sentence prediction, which demands legal deductive reasoning. We evaluate 9 powerful LLMs and find that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles. Categories requiring inferential reasoning, such as motivation and victim-offender relationships, remain the primary bottlenecks. Further analysis reveals substantial gaps between LLMs and human experts, along with pervasive biases in gender, age, and motive attribution. Our findings indicate that current LLMs are far from ready for practical deployment in criminological investigation."
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<abstract>Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest scenarios where the suspect’s identity is already known, leaving the critical pre-arrest challenge of inferring suspect characteristics from incomplete evidence largely unexplored. To fill this gap, we introduce the Profiling, Investigation, and Judgment (PIJ) benchmark, comprising 2,500 real homicide cases from five countries. PIJ evaluates LLMs across three tasks that span the entire criminal investigation pipeline: criminal profiling, which requires abductive reasoning to infer suspect attributes from fragmentary scene evidence, crime process reconstruction, which tests structured information extraction, and sentence prediction, which demands legal deductive reasoning. We evaluate 9 powerful LLMs and find that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles. Categories requiring inferential reasoning, such as motivation and victim-offender relationships, remain the primary bottlenecks. Further analysis reveals substantial gaps between LLMs and human experts, along with pervasive biases in gender, age, and motive attribution. Our findings indicate that current LLMs are far from ready for practical deployment in criminological investigation.</abstract>
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%0 Conference Proceedings
%T Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence
%A Yao, Yutong
%A Yanjie, Cao
%A Chen, Guanhua
%A Xu, Yang
%A Wu, Junchao
%A Wu, Zeyu
%A Chao, Lidia S.
%A Wong, Derek F.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yao-etal-2026-arrest
%X Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest scenarios where the suspect’s identity is already known, leaving the critical pre-arrest challenge of inferring suspect characteristics from incomplete evidence largely unexplored. To fill this gap, we introduce the Profiling, Investigation, and Judgment (PIJ) benchmark, comprising 2,500 real homicide cases from five countries. PIJ evaluates LLMs across three tasks that span the entire criminal investigation pipeline: criminal profiling, which requires abductive reasoning to infer suspect attributes from fragmentary scene evidence, crime process reconstruction, which tests structured information extraction, and sentence prediction, which demands legal deductive reasoning. We evaluate 9 powerful LLMs and find that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles. Categories requiring inferential reasoning, such as motivation and victim-offender relationships, remain the primary bottlenecks. Further analysis reveals substantial gaps between LLMs and human experts, along with pervasive biases in gender, age, and motive attribution. Our findings indicate that current LLMs are far from ready for practical deployment in criminological investigation.
%U https://aclanthology.org/2026.findings-emnlp.2263/
%P 48462-48483
Markdown (Informal)
[Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence](https://aclanthology.org/2026.findings-emnlp.2263/) (Yao et al., Findings 2026)
ACL
- Yutong Yao, Cao Yanjie, Guanhua Chen, Yang Xu, Junchao Wu, Zeyu Wu, Lidia S. Chao, and Derek F. Wong. 2026. Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48462–48483, Budapest, Hungary. Association for Computational Linguistics.