aiqbench analysis
This paper applies LLM-based prompting and retrieval to automated short-answer grading, reporting gains on SAS-Bench against several baselines without any weight updates. The results are benchmark-specific and come from authors' own evaluation setup, so the headline numbers need independent replication across real classrooms and grading policies. For AI readers, the practical point is that retrieval over teacher-graded examples may offer a cheap way to adapt frozen models to local grading conventions.
TeachGrade: Training-Free Short Answer Scoring with Teacher Evidence
arXiv:2610.14900v1 Announce Type: new Abstract: Multi-subject, multi-step scoring requires consistent partial credit across grading conventions. We introduce TeachGrade, a training-free framework using teacher-graded examples. The Task Adapt module builds two frozen memories: Skill captures contextual teacher preferences beyond the rubric; CaseBank supplies local evidence. PreJudge provides retrieval cues; FinalJudge regrades the answer without preliminary scores. On 3,829 SAS-Bench answers across 12 tasks, official macro collaborative consistency score (CCS) rises from Native's 67.99 to 81.32 (+13.33 points), and pooled step exact match from 54.85% to 62.71%. Macro quadratic weighted kappa (QWK) reaches 84.71, exceeding Native, OpenJudge, ProofGrader, and Trojsten by 9.52, 3.50, 11.37, and 17.43 points, respectively, and leading on 8 tasks. Local Qwen3.8-27B gains 8.83 CCS and 4.08 QWK points, improving CCS on all 12 tasks. QWK gains over Native span 6.38--14.65 points on Portuguese automatic essay scoring (AES) and 2.16--2.24 on ASAP-SAS across label views, with lower mean absolute error. These results support reuse across languages, response formats, and backbones without weight updates.
Abstract:Multi-subject, multi-step scoring requires consistent partial credit across grading conventions. We introduce TeachGrade, a training-free framework using teacher-graded examples. The Task Adapt module builds two frozen memories: Skill captures contextual teacher preferences beyond the rubric; CaseBank supplies local evidence. PreJudge provides retrieval cues; FinalJudge regrades the answer without preliminary scores. On 3,829 SAS-Bench answers across 12 tasks, official macro collaborative consistency score (CCS) rises from Native's 67.99 to 81.32 (+13.33 points), and pooled step exact match from 54.85% to 62.71%. Macro quadratic weighted kappa (QWK) reaches 84.71, exceeding Native, OpenJudge, ProofGrader, and Trojsten by 9.52, 3.50, 11.37, and 17.43 points, respectively, and leading on 8 tasks. Local Qwen3.8-27B gains 8.83 CCS and 4.08 QWK points, improving CCS on all 12 tasks. QWK gains over Native span 6.38--14.65 points on Portuguese automatic essay scoring (AES) and 2.16--2.24 on ASAP-SAS across label views, with lower mean absolute error. These results support reuse across languages, response formats, and backbones without weight updates.
Computation and Language (cs.CL)
arXiv:2610.14900 [cs.CL]
(or arXiv:2610.14900v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.14900 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
construction_example/README.md
construction_example/adaptation_request.json
construction_example/adaptation_system_prompt.txt
construction_example/adaptation_user_prompt.txt
construction_example/builder_v30.py
construction_example/case_entry.json
construction_example/compiled_casebank.json
construction_example/compiled_skill.json
construction_example/knowledge_input_full.json
construction_example/knowledge_input_prompt_view.json
construction_example/original_casebank.json
construction_example/original_skill.json
construction_example/skill_entry.json
construction_example/split_ids.json
construction_example/task_specification.json
construction_example/verification.json
supplementary_audit.pdf
task_comparison/README.md
task_comparison/math_casebank.json
task_comparison/math_skill.json
task_comparison/math_specification.json
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Full text under the publisher's open licence · 289 words
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