The messy-resume skills block was collapsed into a single concatenated string with no separators, making the field hard to reuse downstream.
What was measured
Noise in output
Does it add incorrect or hallucinated fields?
decisive for this rankingtransformation
Incorrect or hallucinated fields mean the resume parser is not producing reliable structured data, which is central to the job. (3 of 3 judges)
What was given, what came back
Test input: Messy real-world resume — John Kumar · pdf · group: resume-parsing
Input — what we sent
A poorly formatted, inconsistent real-world resume for John Kumar, used to test robustness against noisy structure, inconsistent dates, and mixed-content sections.
Why this input is hard
- · messy formatting robustness
- · section detection fallback
- · inconsistent date parsing
- · soft-skills extraction
- · noise and hallucination control
Output — unretouched
Also checked on this input — same tool, 3 other criteria
Field coverage✓ WorkedEven on the messy resume, the parser still returned the core coverage set of name, email, phone, work experience, education, and skills.Messy resume handling⚠ StruggledThe tool accepted the messy file, but it degraded badly in structure by merging bullets into a run-on responsibility line instead of preserving clean formatting.Output quality⚠ StruggledThe messy-resume output degraded sharply: responsibilities became a run-on string, skills collapsed into one concatenated line, and references/certifications/hobbies were missing.
Provenance
- Observation
- f3504830-9157-4146-a34d-a211439d3ce7
- Evidence run
- cbbef4db-964c-49fa-a57f-a2977822bdfc
- Study
- Parse resumes into structured data using an API
- Research task
- 86b9jm30n
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "skima-ai",
scenario: "resume-parsing"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 5 other tools
measured on Noise in output
Affinda✗ FailedThe skills output includes hallucinated taxonomy noise, such as Business Education, that does not appear in the resume.CVParserPro✗ FailedThe parser hallucinated unsupported month values in the work date range, expanding 2019–2021 into January 2019 to December 2021.Extracta.ai⚠ StruggledEmits a blank Languages item when the resume has no languages section, leaving an empty placeholder instead of omitting the field.Hireability✗ FailedThe certification field merged three content types — certifications, skills, and references — into one extracted value instead of separating them.HrFlow◐ MixedThe messy parse can bleed location text into a task item, prepending 'Pune' to one extracted responsibility.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com