Tag
This paper presents a curriculum-grounded LLM-as-Judge pipeline for automated question-level marking in high-stakes exam preparation, using syllabus artefacts and marking guidelines to improve consistency and transparency, with preliminary evaluation showing outcomes comparable to human tutors.
This paper introduces EvalAgent, a system that automates the evaluation of AI agents by encoding domain-specific expertise, addressing the limitations of standard coding assistants in this task. It also presents AgentEvalBench, a benchmark for testing evaluation pipelines, and demonstrates significant improvements in evaluation reliability.
This paper introduces SAGE, a hierarchical LLM-based framework for evaluating literary quality through ontology-grounded interpretive dimensions. It demonstrates high reliability and inter-rater agreement in assessing cultural, emotional, and philosophical aspects of narratives, highlighting gaps between human-authored and LLM-generated works.