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Topic Classification on AG News (test)

94.91Accuracy

CUD

Updated 4d ago

Evaluation Results

Method
CUD 2026.02	94.91	-	-	-	-	-
CKD 2026.02	94.72	-	-	-	-	-
FT Teacher 2026.02	94.69	-	-	-	-	-
MGSKD 2026.02	94.64	-	-	-	-	-
AD-KD 2026.02	94.57	-	-	-	-	-
PKD 2026.02	94.54	-	-	-	-	-
PKD 2026.02	94.37	-	-	-	-	-
LKD 2026.02	94.28	-	-	-	-	-
CKD 2026.02	94.24	-	-	-	-	-
LKD 2026.02	94.23	-	-	-	-	-
TinyBERT 2026.02	94.18	-	-	-	-	-
CUD 2026.02	94.11	-	-	-	-	-
AD-KD 2026.02	94.03	-	-	-	-	-
Fusion 2025.12	92.4	92.4	-	-	92.6	92.4
SFT 2025.06	92.29	-	-	-	-	-
Fusion 2025.12	92.2	92.2	-	-	92.3	92.2
Fusion 2025.12	92.2	92.2	-	-	92.4	92.2
RoBERTa 2025.12	92.2	92.2	-	-	92.3	92.2
Fusion 2025.12	92	92	-	-	92.2	92
Fusion 2025.12	91.6	91.6	-	-	91.7	91.6
RoBERTa 2025.12	91.4	91.4	-	-	91.5	91.4
RoBERTa 2025.12	91	91.1	-	-	91.3	91
RoBERTa 2025.12	91	91	-	-	91.1	91
RoBERTa 2025.12	89.8	89.9	-	-	90.2	89.8
CanDistadd 2025.06	89.46	-	-	-	-	-
SuperICL 2025.06	88.79	-	-	-	-	-
CanDistall 2025.06	88.78	-	-	-	-	-
Extra Annotation 2023.07	88.7	-	-	-	-	-
Extra Annotation 2023.07	88.66	-	-	-	-	-
FreeAL 2025.06	88.64	-	-	-	-	-
Distillation 2025.06	88.29	-	-	-	-	-
Few-shot 2025.06	88.05	-	-	-	-	-
SC 2025.06	87.96	-	-	-	-	-
Zero-shot 2025.06	87.24	-	-	-	-	-
CoTAM 2023.07	86.4	-	-	-	-	-
LOTClass 2023.05	86.2	-	-	-	-	-
COTDA 2023.07	86	-	-	-	-	-
CoTAM 2023.07	85.8	-	-	-	-	-
LLM Pseudo Label 2023.07	85.8	-	-	-	-	-
X-Class 2023.05	85.7	-	-	-	-	-
LLM Pseudo Label 2023.07	85.64	-	-	-	-	-
Base 2023.07	85.5	-	-	-	-	-
AnnoLLM 2025.06	85.39	-	-	-	-	-
OpenAI 2025.12	85.3	84.9	-	-	86.8	84.7
FlipDA++ 2023.07	85.2	-	-	-	-	-
OpenAI 2025.12	85.2	84.7	-	-	86.1	84.4
COTDA 2023.07	85.19	-	-	-	-	-
OpenAI 2025.12	85.1	84.7	-	-	86.3	84.6
REGEN 2023.05	85	-	-	-	-	-
KPT 2023.05	84.8	-	-	-	-	-
FlipDA++ 2023.07	84.72	-	-	-	-	-
OpenAI 2025.12	84.1	83.7	-	-	84.9	83.2
OpenAI 2025.12	83.9	83.5	-	-	84.7	83.4
CoT 2025.06	83.05	-	-	-	-	-
AttrPrompt 2023.06	82.74	-	-	-	-	-
No Example 2023.07	81.3	-	-	-	-	-
Base 2023.07	81.05	-	-	-	-	-
ProGen 2023.06	80.81	-	-	-	-	-
SunGen 2023.06	80.49	-	-	-	-	-
Deep-Thinking 2023.05	80.3	-	-	-	-	-
ReGen 2023.06	80.18	-	-	-	-	-
Mining (Re-implementation) 2023.05	79.7	-	-	-	-	-
Mining 2023.05	79.2	-	-	-	-	-
KNN-Prompt 2023.05	78.8	-	-	-	-	-
NSP-BERT 2023.05	78.1	-	-	-	-	-
TE-NLI (Best) 2023.05	78	-	-	-	-	-
LENS 2023.05	77.9	-	-	-	-	-
SuperGen 2023.05	77.4	-	-	-	-	-
SuperGen 2023.06	77.4	-	-	-	-	-
NLI-ST 2023.05	76.5	-	-	-	-	-
ZeroGen 2023.06	76.48	-	-	-	-	-
Random 2023.05	74.6	-	-	-	-	-
SimPrompt 2023.06	74.3	-	-	-	-	-
GPT-3 2023.05	73.9	-	-	-	-	-
Prompt 2023.05	73.2	-	-	-	-	-
Zero-shot Prompting 2023.06	73.2	-	-	-	-	-
Deep-Thinking 2023.05	72.9	-	-	-	-	-
FacilityLocation 2023.05	42.5	-	-	-	-	-
K-Center Greedy 2023.05	42.3	-	-	-	-	-
CAL 2023.05	42.3	-	-	-	-	-
GraphCut 2023.05	41.9	-	-	-	-	-
Herding 2023.05	38.7	-	-	-	-	-
Margin 2023.05	38.1	-	-	-	-	-
CRAIG 2023.05	37.4	-	-	-	-	-
Random 2023.05	33.6	-	-	-	-	-
GradMatch 2023.05	32.6	-	-	-	-	-
F-Ordered 2024.01	32.1	-	-	-	-	-
Batch-ICL 2024.01	31.6	-	-	-	-	-
LeastConfidence 2023.05	31.6	-	-	-	-	-
Batch-ICL 2024.01	31.4	-	-	-	-	-
Entropy 2023.05	30.6	-	-	-	-	-
N-shot ICL 2024.01	30.5	-	-	-	-	-
F-Ordered 2024.01	30.2	-	-	-	-	-
Batch-ICL 2024.01	29.4	-	-	-	-	-
Batch-ICL 2024.01	29.3	-	-	-	-	-
N-shot ICL 2024.01	28.7	-	-	-	-	-
PCW 2024.01	25.8	-	-	-	-	-
PCW 2024.01	25.5	-	-	-	-	-
RoBERTa-large (Original) 2023.05	-	-	53	-	-	-
RoBERTa-large (Context Calibration) 2023.05	-	-	59.1	-	-	-

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