Grading recipes without calibration examples perform substantially worse on MAE, though multi-step without examples ranks second on QWK¶
Claim · All claims
Evidence · 1 study · causalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Single-step NoEx showed MAE of approximately 1.45 and Multi-step NoEx the highest at 1.55, yet Multi-step NoEx performed second-best in QWK (around 0.35). → Olga Manakina 2026
Evidence¶
Olga Manakina 2026¶
Olga Manakina, Igor Bogdanov. (2026). Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring. https://arxiv.org/abs/2608.23814 doi:10.48550/arxiv.2608.23814
q2 · i? · causal · r1
MAE analysis across the four grading recipes in the MAB experiment on IELTS Task 2 essays. The article reports the no-example recipes performed worse on MAE, while noting "Multi-step NoEx performed second-best in QWK (around 0.35), despite its poor MAE."
"The approaches without examples performed substantially worse, with Single-step NoEx showing an MAE of approximately 1.45 and Multi-step NoEx demonstrating the highest error rate at 1.55."
Discussion¶
Related Claims¶
- The four grading recipes exhibit an accuracy-cost tradeoff, with the most accurate recipe costing roughly five times the balanced alternative — related
- A multi-armed bandit controller achieves scoring accuracy comparable to exhaustive grid search while reducing LLM calls by 78.4% and token consumption by 72.8% — related
- The multi-step grading recipe with calibration examples achieves the highest scoring accuracy and receives the majority of bandit arm pulls — related
- Removing detailed rubric explanations from prompts improved grading accuracy across all approaches in an ablation study — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
no-example-recipes-worse-mae |
title |
Grading recipes without calibration examples perform substantially worse on MAE, though multi-step without examples ranks second on QWK |
description |
Grading recipes without calibration examples perform substantially worse on MAE, though multi-step without examples ranks second on QWK |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — olga-manakina-2026 |
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