I Think, Therefore I Am Under-Qualified? A Benchmark for Evaluating Linguistic Shibboleth Detection in LLM Hiring Evaluations

TL;DR

This paper presents a benchmark to evaluate how Large Language Models respond to subtle linguistic markers that can reveal demographic attributes, highlighting biases in automated evaluations.

Contribution

The authors develop a controlled benchmark with linguistic variations to measure demographic bias and discrimination in LLMs' responses, focusing on hedging language.

Findings

Hedged responses received 25.6% lower ratings on average.

The benchmark effectively identifies model-specific biases.

Linguistic markers can systematically influence model evaluations.

Abstract

This paper introduces a comprehensive benchmark for evaluating how Large Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic markers that can inadvertently reveal demographic attributes such as gender, social class, or regional background. Through carefully constructed interview simulations using 100 validated question-response pairs, we demonstrate how LLMs systematically penalize certain linguistic patterns, particularly hedging language, despite equivalent content quality. Our benchmark generates controlled linguistic variations that isolate specific phenomena while maintaining semantic equivalence, which enables the precise measurement of demographic bias in automated evaluation systems. We validate our approach along multiple linguistic dimensions, showing that hedged responses receive 25.6% lower ratings on average, and demonstrate the benchmark's effectiveness in identifying model-specific biases. This work establishes a foundational framework for detecting and measuring linguistic discrimination in AI systems, with broad applications to fairness in automated decision-making contexts.