Papers › Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes

Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes

6 May 2024arXiv:2405.06687archive 2025-07-28

Damin Zhang, Yi Zhang, Geetanjali Bihani, Julia Rayz

With the impressive performance in various downstream tasks, large language models (LLMs) have been widely integrated into production pipelines, like recruitment and recommendation systems. A known issue of models trained on natural language data is the presence of human biases, which can impact the fairness of the system. This paper investigates LLMs' behavior with respect to gender stereotypes, in the context of occupation decision making. Our framework is designed to investigate and quantify the presence of gender stereotypes in LLMs' behavior via multi-round question answering. Inspired by prior works, we construct a dataset by leveraging a standard occupation classification knowledge base released by authoritative agencies. We tested three LLMs (RoBERTa-large, GPT-3.5-turbo, and Llama2-70b-chat) and found that all models exhibit gender stereotypes analogous to human biases, but with different preferences. The distinct preferences of GPT-3.5-turbo and Llama2-70b-chat may imply the current alignment methods are insufficient for debiasing and could introduce new biases contradicting the traditional gender stereotypes.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2405.06687")

Code

Syntology Ran 10 of 13 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 10 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 10 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

daminz97/multi-step_gsv officialmentioned in paper report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

13 samples harvested; 10 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

10ran
3unverified

Licence: 13 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from daminz97/multi-step_gsv. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

batch_annotate daminz97/multi-step_gsv/llama3.1_result.py official repository ran no licence file found · pointer only · ca8d7449757a6def · report
call_api_async daminz97/multi-step_gsv/llama3.1_result.py official repository ran no licence file found · pointer only · a840df921741f9a6 · report
fill_additional_context daminz97/multi-step_gsv/gpt_result.py official repository ran no licence file found · pointer only · 5ea3171baa815c88 · report
fill_additional_context daminz97/multi-step_gsv/roberta_result.py official repository ran no licence file found · pointer only · 31e698f63447017e · report
inference daminz97/multi-step_gsv/gpt_prompt.py official repository ran no licence file found · pointer only · 110edef5e59a4355 · report
inference daminz97/multi-step_gsv/gpt_result.py official repository ran no licence file found · pointer only · 94dabb22d3d3dcb6 · report
inference daminz97/multi-step_gsv/roberta_prompt.py official repository ran no licence file found · pointer only · 28baa00f5f01d782 · report
inference daminz97/multi-step_gsv/roberta_result.py official repository ran no licence file found · pointer only · 62fa0ab3fc25e144 · report
process_subject daminz97/multi-step_gsv/gpt_prompt.py official repository ran no licence file found · pointer only · 86179389ceb2b12f · report
process_subject daminz97/multi-step_gsv/roberta_prompt.py official repository ran no licence file found · pointer only · 43ec2b9c2ffcea94 · report
batch_annotate daminz97/multi-step_gsv/llama3.1_prompt.py official repository unverified no licence file found · pointer only · c9c0eace89d1838d · report
call_api_async daminz97/multi-step_gsv/llama3.1_prompt.py official repository unverified no licence file found · pointer only · 26478cb6705a1f7b · report
process_attr_question daminz97/multi-step_gsv/llama3.1_prompt.py official repository unverified no licence file found · pointer only · 86e07f547cbfad57 · report

Tasks

Decision MakingFairnessQuestion AnsweringRecommendation Systems

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

AdamAttentionAttention DropoutBASEBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections