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New LLM frameworks and benchmarks advance automatic short answer scoring

By PulseAugur Editorial · [2 sources] ·

Two new research papers introduce novel frameworks for automatic short answer scoring (ASAS) using large language models (LLMs). The first paper, RUSPAN, treats rubric descriptions as semantic label representations and serializes question context, student answers, and rubric levels into a single sequence for scoring. It also introduces a Rubric-Independent Mask (RIM) to improve zero-shot transfer across different rubric sets. The second paper, Alice, presents a large-scale German benchmark for rubric-based ASAS, focusing on learning performance, knowledge elements, and skills, and benchmarks various language models, noting that LLMs struggle with zero-shot scoring of knowledge elements and skills. AI

IMPACT These advancements could improve the efficiency and accuracy of automated grading systems, potentially freeing up educators' time for more complex tasks.

RANK_REASON Two research papers published on arXiv introducing new methods and benchmarks for automatic short answer scoring.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New LLM frameworks and benchmarks advance automatic short answer scoring

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zhifan Sun, Sebastian Gombert, Fabian Zehner, Leon Camus, Longwei Cong, Hendrik Drachsler ·

    Rubric Spans are Label Representations: Joint LLM Encoding for Short Answer Scoring

    arXiv:2610.09660v1 Announce Type: new Abstract: Automatic Short Answer Scoring (ASAS) requires models that can score student responses against question-specific criteria while remaining efficient and transferable across rubric sets. We propose RUSPAN, a rubric-conditioned ASAS fr…

  2. arXiv cs.CL TIER_1 English(EN) · Zhifan Sun, Sebastian Gombert, Jannik Lossjew, Tobias Wyrwich, Berrit Katharina Czinczel, David Bednorz, Marcus Kubsch, Knut Neumann, Hendrik Drachsler ·

    Alice: A Large-Scale German Benchmark for Rubric-Based Multi-Dimensional Automatic Short Answer Scoring

    arXiv:2610.09661v1 Announce Type: new Abstract: Automatic Short Answer Scoring (ASAS) is central to NLP for Education. However, openly available benchmarks remain scarce, and existing datasets largely address how well students answer a question directly rather than how well they …

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