Compare the speed and accuracy of automated resume screening using specialized ATS intelligence tools versus a fine-tuned open source LLM agent.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Compare the speed and accuracy of automated resume screening using specialized ATS intelligence tools versus a fine-tuned open source LLM agent.
3 real searches · 1 real-search pass
benchmark automated resume screening applicant tracking systems large language models accuracy speed hiring researchGoogle ↗
OBSERVEDComparison
LLM resume screening candidate ranking bias study recruitment 2024 2025Google ↗
OBSERVEDOther / navigational
EEOC AI hiring tools technical assistance adverse impact selection proceduresGoogle ↗
OBSERVEDConsequence
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
benchmark automated resume screening applicant tracking systems large language models accuracy speed hiring research Google ↗
LLM resume screening candidate ranking bias study recruitment 2024 2025 Google ↗
EEOC AI hiring tools technical assistance adverse impact selection procedures Google ↗
- Application of LLM Agents in Recruitment: A Novel Framework for Automated Resume Screening · jstage.jst.go.jp
- Design and effectiveness evaluation of an intelligent resume screening system for enterprise recruitment based on large models · sciencedirect.com
- Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions · arxiv.org
- Human and LLM-Based Resume Matching: An Observational Study · aclanthology.org
- Measuring gender and racial biases in large language models: Intersectional evidence from automated resume evaluation | PNAS Nexus · academic.oup.com
- Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts · aclanthology.org
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
benchmark automated resume screening applicant tracking systems large language models accuracy speed hiring research OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
LLM resume screening candidate ranking bias study recruitment 2024 2025 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
EEOC AI hiring tools technical assistance adverse impact selection procedures OBSERVED | Google ↗ AI Mode ↗ | — | Consequence | — | — | 1x of 1 |