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Evidence record 4737 · automatically gathered

Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation

Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. This paradigm leverages LLMs broad world knowledge and ease of deployment, but limited task-specific data may reduce alignment on complex scoring tasks. In particular, its impact on scoring partially correct responses that require nuanced interpretation remains underexplored. We investigate the relationship between the degree of task-specific adaptat

Record details

Published: 8 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Finance, VC & PE
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (8 May 2026), “Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation,” evidence record 4737, https://ethics.ai/record/4737 (originally published by arXiv).

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