@inproceedings{shi-etal-2026-peoplesearchbench,
title = "{P}eople{S}earch{B}ench: Evaluating {AI}-Powered People Search Platforms with Criteria-Grounded Verification",
author = "Shi, Tianyu and
Wang, Wei and
Xie, Zequn and
Zhang, Shuai and
Xia, Boyang and
Zeng, Chenyu and
Zhang, QI and
Ai, Lynn and
Yaqi, YU and
Zhang, Kaiming and
Tang, Feiyue and
Yu, Zhenyu and
Ding, Lei",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.66/",
pages = "1111--1122",
ISBN = "979-8-89176-460-6",
abstract = "Al-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized benchmark exists for their rigorous evaluation. We present PEOPLESEARCHBENCH, an open-source benchmark comprising 119 multilingual queries across four scenarios: corporate recruiting, B2B sales prospecting, expert search, and influencer discovery. A central contribution is Criteria-Grounded Verification, an evaluation methodology that decomposes each query into explicit, independently checkable criteria and verifies each returned individual via live web search, producing factual relevance judgments rather than subjective LLM-as-judge scores. We evaluate four architecturally diverse platforms along three complementary dimensions-Relevance Precision, Effective Coverage, and Information Utility-and find that multi-source search agents significantly outperform single-domain systems, particularly in influencer discovery where the performance gap is largest. Platform rankings are robust across ablations on scoring thresholds, dimension weights, and judge models. All code, queries, and evaluation prompts are publicly available."
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<abstract>Al-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized benchmark exists for their rigorous evaluation. We present PEOPLESEARCHBENCH, an open-source benchmark comprising 119 multilingual queries across four scenarios: corporate recruiting, B2B sales prospecting, expert search, and influencer discovery. A central contribution is Criteria-Grounded Verification, an evaluation methodology that decomposes each query into explicit, independently checkable criteria and verifies each returned individual via live web search, producing factual relevance judgments rather than subjective LLM-as-judge scores. We evaluate four architecturally diverse platforms along three complementary dimensions-Relevance Precision, Effective Coverage, and Information Utility-and find that multi-source search agents significantly outperform single-domain systems, particularly in influencer discovery where the performance gap is largest. Platform rankings are robust across ablations on scoring thresholds, dimension weights, and judge models. All code, queries, and evaluation prompts are publicly available.</abstract>
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%0 Conference Proceedings
%T PeopleSearchBench: Evaluating AI-Powered People Search Platforms with Criteria-Grounded Verification
%A Shi, Tianyu
%A Wang, Wei
%A Xie, Zequn
%A Zhang, Shuai
%A Xia, Boyang
%A Zeng, Chenyu
%A Zhang, Q. I.
%A Ai, Lynn
%A Yaqi, Y. U.
%A Zhang, Kaiming
%A Tang, Feiyue
%A Yu, Zhenyu
%A Ding, Lei
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F shi-etal-2026-peoplesearchbench
%X Al-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized benchmark exists for their rigorous evaluation. We present PEOPLESEARCHBENCH, an open-source benchmark comprising 119 multilingual queries across four scenarios: corporate recruiting, B2B sales prospecting, expert search, and influencer discovery. A central contribution is Criteria-Grounded Verification, an evaluation methodology that decomposes each query into explicit, independently checkable criteria and verifies each returned individual via live web search, producing factual relevance judgments rather than subjective LLM-as-judge scores. We evaluate four architecturally diverse platforms along three complementary dimensions-Relevance Precision, Effective Coverage, and Information Utility-and find that multi-source search agents significantly outperform single-domain systems, particularly in influencer discovery where the performance gap is largest. Platform rankings are robust across ablations on scoring thresholds, dimension weights, and judge models. All code, queries, and evaluation prompts are publicly available.
%U https://aclanthology.org/2026.emnlp-industry.66/
%P 1111-1122
Markdown (Informal)
[PeopleSearchBench: Evaluating AI-Powered People Search Platforms with Criteria-Grounded Verification](https://aclanthology.org/2026.emnlp-industry.66/) (Shi et al., EMNLP 2026)
ACL
- Tianyu Shi, Wei Wang, Zequn Xie, Shuai Zhang, Boyang Xia, Chenyu Zeng, QI Zhang, Lynn Ai, YU Yaqi, Kaiming Zhang, Feiyue Tang, Zhenyu Yu, and Lei Ding. 2026. PeopleSearchBench: Evaluating AI-Powered People Search Platforms with Criteria-Grounded Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1111–1122, Budapest, Hungary. Association for Computational Linguistics.