Few-shot prompting does not improve diagnostic F1 or accuracy but reduces evidence over-attribution and false-attribution¶
Claim · All claims
Evidence · 1 study · causalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
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? Few-shot prompting did reduce tendencies toward evidence over-attribution and false-attribution. → Yoonsu Kim 2026
Evidence¶
Yoonsu Kim 2026¶
Yoonsu Kim, Hyoungwook Jin, Hayeon Doh, Eunhye Kim, Dongyun Jung, Seungju Kim, Kiyoon Choi, Jinho Son, and Juho Kim. (2026). Benchmarking Large Language Models for Diagnosing Students' Cognitive Skills from Handwritten Math Work. arXiv. https://arxiv.org/abs/2504.00843 doi:10.48550/arxiv.2504.00843
q2 · i? · causal · r1
Same few-shot experiment. The article reports reduced over-attribution and false-attribution, suggesting examples encourage more cautious, evidence-grounded responses even while accuracy stays limited.
"However, few-shot prompting did reduce tendencies toward evidence over-attribution and false-attribution, indicating that examples may encourage more cautious, evidence-grounded responses even when overall diagnostic accuracy remains limited."
Discussion¶
Related Claims¶
- In-context examples improve LLM Bloom classification: three-shot and ten-shot prompting outperform zero-shot across datasets — reports the opposite
- LLMs diagnose Applying skills better than Knowing skills, but with higher evidence over-attribution and false-attribution — related
- In-context learning did not consistently improve LLM annotation reliability over zero-shot prompting for engagement coding — related
- MathCog: a benchmark of 3,036 teacher-annotated diagnostic verdicts over 639 handwritten student responses to 110 math problems with Evident/Vague evidence labels — related
- LLMs frequently over-attribute evidential strength and assert Evident evidence even for incorrect diagnoses, and the two tendencies are strongly correlated — related
- Curricular chain-of-thought prompting improves competency-classification accuracy over zero-shot, with gains concentrated in larger models, while definition-based prompting does not improve performance — related
- All 18 evaluated LLMs score macro F1 below 0.5 in diagnosing students' cognitive skills from handwritten math work — related
- LLM diagnostic performance drops sharply when student responses contain vague rather than evident evidence — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
few-shot-prompting-no-f1-gain-reduces-overattribution |
title |
Few-shot prompting does not improve diagnostic F1 or accuracy but reduces evidence over-attribution and false-attribution |
description |
Few-shot prompting does not improve diagnostic F1 or accuracy but reduces evidence over-attribution and false-attribution |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — yoonsu-kim-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.