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Sarcasm Detection on SinaSarc (test)

91.44Accuracy

GAN and LLM-driven data augmentation framework

Updated 8d ago

Evaluation Results

Method	Links
GAN and LLM-driven data augmentation framework 2026.04		91.44	92.08	90.69	91.38	90.83	92.2	91.51
RoBERTa-large 2026.04		88.61	89.15	91.87	90.49	87.78	83.92	85.8
RoBERTa-base 2026.04		88	89.22	90.59	89.9	86.16	84.27	85.21
BERT-large 2026.04		87.35	88.04	90.89	89.44	86.27	82.25	84.21
BERT-base 2026.04		86.27	86.8	90.46	88.6	85.41	80.23	82.74
Baichuan2-7B 2026.04		85.78	87.98	87.9	87.94	82.63	82.74	82.69
Qwen2-7B 2026.04		85.74	84.8	87.1	85.93	86.74	84.38	85.54
FNN 2026.04		84.64	86.1	88.2	87.14	82.42	79.53	80.95
LSTM 2026.04		84.28	86.5	86.92	86.71	81.06	80.49	80.78
BiLSTM-Att 2026.04		84.14	85.98	87.35	86.66	81.38	79.53	80.44
GPT-4-Turbo 2026.04		83.78	86.38	86.06	86.22	80.08	80.5	80.29
BiLSTM 2026.04		83.71	85.45	87.22	86.33	81.07	78.65	79.84
Gemini-1.5-Pro 2026.04		83.37	84.58	87.8	86.16	81.46	77.01	79.17
LSTM-Att 2026.04		83.35	85.15	86.92	86.03	80.62	78.21	79.39
Mixtral 8x7B 2026.04		83.32	84.33	88.08	86.16	81.7	76.48	79.01
SBERT 2026.04		82.98	84.11	87.71	85.88	81.18	76.19	78.6
SVM 2026.04		81.83	85.69	83.07	84.36	76.68	80.05	78.33
Random Forest 2026.04		80.32	79.85	89.12	84.23	81.22	67.66	73.83
AdaBoost 2026.04		72.31	75.06	79.46	77.2	67.75	62.04	64.77