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Evidence record 10982 · automatically gathered

Investigating first-language bias in LLM-based automated essay scoring: A cross-prompt evaluation of an open-weight AI-model on TOEFL essays

arXiv:2607.14605v1 Announce Type: cross Abstract: This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-adapted open-weight large language model (Gemma-3-27B-it) applied to automated essay scoring. Using the identical model and inference configuration reported in "AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models" (Gayed, 2026), which was fine-tuned on 480 argumentative essays from two prompts

Record details

Published: 17 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 17 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

Investigating first-language bias in LLM-based automated essay scoring: A cross-prompt evaluation of an open-weight AI-model on TOEFL essays

arXiv · 16 July 2026

Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

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arXiv · 21 July 2026

Shared topic: Bias & fairness · Shared topic: Finance, VC & PE

Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs

arXiv · 21 July 2026

How to cite this record

ethics.ai (17 July 2026), “Investigating first-language bias in LLM-based automated essay scoring: A cross-prompt evaluation of an open-weight AI-model on TOEFL essays,” evidence record 10982, https://ethics.ai/record/10982 (originally published by arXiv cs.CY).

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