Opinion Polarity Detection on MPQA
93.3AccuracyAdaSent
Evaluation Results
| Method | Links | |
|---|---|---|
| AdaSent2015.04 | 93.3 | |
| ADASENT2017.04 | 93.3 | |
| FADS-ICLNumber of training shots (m)=4, LLM scale=1.5B2024.05 | 91.1 | |
| BRNN2015.04 | 90.3 | |
| LM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 90.15 | |
| RNN2015.04 | 90.1 | |
| STM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 89.95 | |
| NLM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 89.92 | |
| STM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 89.83 | |
| LSTM-400Emb.Size=4002019.05 | 89.73 | |
| NLM+TSEDEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 89.63 | |
| NLM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 89.61 | |
| CNN-staticword vectors=static pre-trained (word2vec)2014.08 | 89.6 | |
| CNN2015.04 | 89.6 | |
| CNN2017.04 | 89.6 | |
| STM+TSEDEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 89.6 | |
| LM+TSEDEmb.Size=50, Teacher Model Architecture=LSTM2019.05 | 89.59 | |
| CNN-non-staticword vectors=fine-tuned pre-trained (word2vec)2014.08 | 89.5 | |
| LM+TSED+PT+2LEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 89.44 | |
| CNN-multichannelword vectors=pre-trained (word2vec), channels=22014.08 | 89.4 | |
| SKIPTHOUGHTLayer Normalization=True2017.04 | 89.3 | |
| LM+TSEDEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 89.14 | |
| STM+TSEDEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 89.13 | |
| ICLBackbone=Llama-2 70B, Shots=128-shots2024.05 | 89.1 | |
| NLM+TSEDEmb.Size=50, Teacher Model Architecture=CNN2019.05 | 88.9 | |
| CNN-400Emb.Size=4002019.05 | 88.78 | |
| IS-BERT_baseBackbone=BERT-base2022.03 | 88.75 | |
| SCD-BERT_baseBackbone=BERT-base2022.03 | 88.67 | |
| Avg. BERT embeddingsBackbone=BERT-base, Pooling=Avg2022.03 | 88.66 | |
| ENCEmb.Size=502019.05 | 88.65 | |
| SimCSE-BERT_baseBackbone=BERT-base2022.03 | 88.6 | |
| BYTE MLSTMClassifier=Logistic Regression, Penalty=L12017.04 | 88.5 | |
| BERT [CLS]-embeddingBackbone=BERT-base, Pooling=CLS2022.03 | 88.21 | |
| SCD-RoBERTa_baseBackbone=RoBERTa-base2022.03 | 88.19 | |
| USE_T+DAN (w2v w.e.)Universal Encoder=Transformer (USE_T), Transfer Level=Sentence & Word, Embedding Initialization=word2vec skip-gram, Transfer Task Model=DAN2018.03 | 88.14 | |
| FADS-ICLNumber of training shots (m)=32, LLM scale=1.5B2024.05 | 88 | |
| FADS-ICLBackbone=Llama-2 13B, Shots=128-shots2024.05 | 88 | |
| SKIPTHOUGHT2017.04 | 87.9 | |
| GloVe embeddings (avg.)Model=GloVe (avg.)2022.03 | 87.85 | |
| FADS-ICLBackbone=Llama-2 70B, Shots=128-shots2024.05 | 87.7 | |
| GPT Large FTBackbone=GPT Large, Shots=N/A (Fine-tuned)2024.05 | 87.6 | |
| ICLBackbone=Llama-1 30B, Shots=128-shots2024.05 | 87.6 | |
| USE_T+CNN (w2v w.e.)Universal Encoder=Transformer (USE_T), Transfer Level=Sentence & Word, Embedding Initialization=word2vec skip-gram, Transfer Task Model=CNN2018.03 | 87.32 | |
| FADS-ICLBackbone=Llama-2 7B, Shots=128-shots2024.05 | 87.3 | |
| FADS-ICLBackbone=Llama-1 30B, Shots=128-shots2024.05 | 87.3 | |
| CCAE2014.08 | 87.2 | |
| DictRep BOW+embs.Data Source Type=Other structured data resource2016.02 | 87.2 | |
| USE_T+DAN (lrn w.e.)Universal Encoder=Transformer (USE_T), Transfer Level=Sentence, Embedding Initialization=learned on transfer task, Transfer Task Model=DAN2018.03 | 87.14 | |
| SkipThoughtData Source Type=Ordered Sentences2016.02 | 87.1 | |
| Skip-thoughtModel=Skip-thought2022.03 | 87.1 | |
| USE_D+DAN (w2v w.e.)Universal Encoder=Deep Averaging Network (USE_D), Transfer Level=Sentence & Word, Embedding Initialization=word2vec skip-gram, Transfer Task Model=DAN2018.03 | 87.01 | |
| ICLBackbone=Llama-2 7B, Shots=128-shots2024.05 | 87 | |
| USE TUniversal Encoder=Transformer (USE_T), Transfer Level=Sentence, Embedding Initialization=None, Transfer Task Model=None2018.03 | 86.98 | |
| SDAE+embs.Data Source Type=Unordered Sentences2016.02 | 86.9 | |
| SDAE2017.04 | 86.9 | |
| USE_T+CNN (lrn w.e.)Universal Encoder=Transformer (USE_T), Transfer Level=Sentence, Embedding Initialization=learned on transfer task, Transfer Task Model=CNN2018.03 | 86.85 | |
| ICLBackbone=Llama-1 7B, Shots=128-shots2024.05 | 86.8 | |
| SimCSE-RoBERTa_baseBackbone=RoBERTa-base2022.03 | 86.63 | |
| RAE2014.08 | 86.4 | |
| RAE2015.04 | 86.4 | |
| CBOW2015.04 | 86.4 | |
| CNN-50Emb.Size=502019.05 | 86.4 | |
| Sent-Parser2014.08 | 86.3 | |
| NBSVM2014.08 | 86.3 | |
| MNB2014.08 | 86.3 | |
| F-Dropout2014.08 | 86.3 | |
| NB-SVM2015.04 | 86.3 | |
| MNB2015.04 | 86.3 | |
| NBSVM2017.04 | 86.3 | |
| SAE+embs.Data Source Type=Unordered Sentences2016.02 | 86.2 | |
| CPHRASEData Source Type=2.8B words2016.02 | 86.2 | |
| G-Dropout2014.08 | 86.1 | |
| Tree-CRF2014.08 | 86.1 | |
| USE_D+DAN (lrn w.e.)Universal Encoder=Deep Averaging Network (USE_D), Transfer Level=Sentence, Embedding Initialization=learned on transfer task, Transfer Task Model=DAN2018.03 | 85.97 | |
| FADS-ICLBackbone=Llama-1 13B, Shots=128-shots2024.05 | 85.9 | |
| USE_D+CNN (w2v w.e.)Universal Encoder=Deep Averaging Network (USE_D), Transfer Level=Sentence & Word, Embedding Initialization=word2vec skip-gram, Transfer Task Model=CNN2018.03 | 85.87 | |
| FADS-ICLBackbone=Llama-1 7B, Shots=128-shots2024.05 | 85.8 | |
| LSTM-50Emb.Size=502019.05 | 85.66 | |
| USE_D+CNN (lrn w.e.)Universal Encoder=Deep Averaging Network (USE_D), Transfer Level=Sentence, Embedding Initialization=learned on transfer task, Transfer Task Model=CNN2018.03 | 85.53 | |
| ICLBackbone=Llama-1 13B, Shots=128-shots2024.05 | 85.5 | |
| ICLBackbone=Llama-2 13B, Shots=128-shots2024.05 | 85.5 | |
| kNN-promptBackbone=Llama-2 13B, Shots=128-shots2024.05 | 85.5 | |
| USE DUniversal Encoder=Deep Averaging Network (USE_D), Transfer Level=Sentence, Embedding Initialization=None, Transfer Task Model=None2018.03 | 85.38 | |
| FADS-ICLNumber of training shots (m)=128, LLM scale=1.5B2024.05 | 84.8 | |
| FADS-ICLBackbone=GPT-2 1.5B, Shots=128-shots2024.05 | 84.8 | |
| FADS-ICLcandidate pool size (m)=1282024.05 | 84.8 | |
| kNN-promptingBackbone=Llama-1 7B, Shots=128-shots2024.05 | 84.6 | |
| GrConv2015.04 | 84.5 | |
| kNN-promptingBackbone=Llama-2 13B, Shots=128-shots2024.05 | 84.5 | |
| kNN-promptingBackbone=Llama-1 13B, Shots=128-shots2024.05 | 84.4 | |
| kNN-promptBackbone=Llama-1 13B, Shots=128-shots2024.05 | 84.3 | |
| kNN-promptBackbone=Llama-1 30B, Shots=128-shots2024.05 | 84.3 | |
| kNN-promptingBackbone=Llama-1 30B, Shots=128-shots2024.05 | 84.3 | |
| kNN-promptBackbone=Llama-2 70B, Shots=128-shots2024.05 | 84.3 | |
| FADS-ICLcandidate pool size (m)=2562024.05 | 84.2 | |
| RoBERTa [CLS]-embeddingBackbone=RoBERTa-base, Pooling=CLS2022.03 | 84.18 | |
| kNN-promptingBackbone=Llama-2 70B, Shots=128-shots2024.05 | 84.1 | |
| ICLNumber of training shots (m)=16, LLM scale=1.5B2024.05 | 83.9 | |
| kNN-promptingNumber of training shots (m)=32, LLM scale=1.5B2024.05 | 83.9 | |
| GNN2025.07 | 83.73 |