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ARXIV:2603.18765 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18765LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALERudra Jadhav · Janhavi Danve · Sonalika Shaw · arXiv
Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities.
Opportunity summary
Pain Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the underlying…
As large language models (LLMs) are increasingly deployed as automated graders in educational settings, concerns about fairness and bias in their evaluations have become critical. This study investigates whether LLMs exhibit implicit grading bias…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal statistically significant grading bias in Essay/Writing tasks across both models and all perturbation types (p < 0.05), with effect sizes ranging…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities.
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10.48550/arXiv.2603.18765Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities.
Abstract
As large language models (LLMs) are increasingly deployed as automated graders in educational settings, concerns about fairness and bias in their evaluations have become critical. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the underlying content correctness remains constant. We constructed a controlled dataset of 180 student responses across three subjects (Mathematics, Programming, and Essay/Writing), each with three surface-level perturbation types: grammar errors, informal language, and non-native phrasing. Two state-of-the-art open-source LLMs -- LLaMA 3.3 70B (Meta) and Qwen 2.5 72B (Alibaba) -- were prompted to grade responses on a 1-10 scale with explicit instructions to evaluate content correctness only and to disregard writing style. Our results reveal statistically significant grading bias in Essay/Writing tasks across both models and all perturbation types (p < 0.05), with effect sizes ranging from medium (Cohen's d = 0.64) to very large (d = 4.25). Informal language received the heaviest penalty, with LLaMA deducting an average of 1.90 points and Qwen deducting 1.20 points on a 10-point scale -- penalties comparable to the difference between a B+ and C+ letter grade. Non-native phrasing was penalized 1.35 and 0.90 points respectively. In sharp contrast, Mathematics and Programming tasks showed minimal bias, with most conditions failing to reach statistical significance. These findings demonstrate that LLM grading bias is subject-dependent, style-sensitive, and persists despite explicit counter-bias instructions in the grading prompt. We discuss implications for equitable deployment of LLM-based grading systems and recommend bias auditing protocols before institutional adoption.
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Dimensions overall score 7.0
PROBLEM
Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the underlying content correctness remains constant.
METHOD
As large language models (LLMs) are increasingly deployed as automated graders in educational settings, concerns about fairness and bias in their evaluations have become critical. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal statistically significant grading bias in Essay/Writing tasks across both models and all perturbation types (p < 0.05), with effect sizes ranging from medium (Cohen's d = 0.64) to very...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the underlying content correctness remains constant.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As large language models (LLMs) are increasingly deployed as automated graders in educational settings, concerns about fairness and bias in their evaluations have become critical. This study investigates whether LLMs exhibit implicit grading bias based on writing style when the underlying content correctness remains constant.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our results reveal statistically significant grading bias in Essay/Writing tasks across both models and all perturbation types (p < 0.05), with effect sizes ranging from medium (Cohen's d = 0.64) to very large (d = 4.25). Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop an LLM bias auditing tool to ensure fair automated grading by identifying and quantifying style-based grading disparities.
Segment
LLM Evaluation
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